The DOI matches its Crossref record; 77 of the 78 references it cites were found in a registry or an index; 15 of the 44 sources its claims cite were checked for support.
Topology and data
Open accessAbstract
An important feature of modern science and engineering is that data of various kinds is being produced at an unprecedented rate. This is so in part because of new experimental methods, and in part because of the increase in the availability of high powered computing technology. It is also clear that the nature of the data we are obtaining is significantly different. For example, it is now often the case that we are given data in the form of very long vectors, where all but a few of the coordinates turn out to be irrelevant to the questions of interest, and further that we don’t necessarily know which coordinates are the interesting ones. A related fact is that the data is often very high-dimensional, which severely restricts our ability to visualize it. The data obtained is also often much noisier than in the past and has more missing information (missing data). This is particularly so in the case of biological data, particularly high throughput data from microarray or other sources. Our ability to analyze this data, both in terms of quantity and the nature of the data, is clearly not keeping pace with the data being produced. In this paper, we will discuss how geometry and topology can be applied to make useful contributions to the analysis of various kinds of data. Geometry and topology are very natural tools to apply in this direction, since geometry can be regarded as the study of distance functions, and what one often works with are distance functions on large finite sets of data. The mathematical formalism which has been developed for incorporating geometric and topological techniques deals with point clouds, i.e. finite sets of points equipped with a distance function. It then adapts tools from the various branches of geometry to the study of point clouds. The point clouds are intended to be thought of as finite samples taken from a geometric object, perhaps with noise. Here are some of the key points which come up when applying these geometric methods to data analysis. • Qualitative information is needed: One important goal of data analysis is to allow the user to obtain knowledge about the data, i.e. to understand how it is organized on a large scale. For example, if we imagine that we are looking at a data set constructed somehow from diabetes patients, it would be important to develop the understanding that there are two types of the disease, namely the juvenile and adult onset forms. Once that is established, one of course wants to develop quantitative methods for distinguishing them, but the first insight about the distinct forms of the disease is key.
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Claims the paper makes
53 No basis given · 38 Own interpretation · 23 Own result · 17 No full text of the sources · 9 Supported · 5 Partly supported · 5 Sources not found · 1 Couldn’t confirm · 1 Not checked yet · accuracy not yet measured
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Much of current research efforts are focused in this direction (see, e.g., [43] and [39]), and functoriality provides the right general mathematical framework for addressing them.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Could not verifycites [43] von Luxburg, Ulrike, 2008, Consistency of spectral clustering, Ann. Statist. sourceExperimental — accuracy not yet measured
The Introduction discusses questions about whether clustering algorithms converge, and Section 5.2 constructs operators for analyzing spectral clustering. Neither passage addresses functoriality or presents it as a framework for resolving ambiguities in clustering; searches for those concepts found no supporting passage in our copy. (LLM, conf=0.76)
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
Every source the paper cites for this claim was checked and supports it.
In the paper
“We argue that, as suggested in [46], it has a role to play in the study of point cloud data as well, and we give two illustrations of how this could happen, within the context of clustering.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Supportedcites [46] McCullagh, Peter, 2002, What is a statistical model?, Ann. Statist. source
Section 8, “Extensive response variable” (page unavailable), describes a point process as a countable set of points and treats Poisson point-process models as natural transformations of functors. That supports the cited suggestion that functoriality could be useful for point-cloud data; the citing authors present clustering as their own proposed application. (LLM, conf=0.86)
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Niyogi, Smale, and Weinberger in [52] provide sufficient hypotheses which guarantee that this is possible for Riemannian manifolds.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
What the sources say
- Partly supportedcites [52] Niyogi, Partha, 2008, Finding the homology of submanifolds with high confidence from random samples, Discrete Comput. Geom. sourceExperimental — accuracy not yet measured
Section 2 (Preliminaries; page unavailable) considers compact Riemannian submanifolds sampled as data. Section 3 (An Outline of Our Main Results; page unavailable) gives conditions under which the samples recover the manifold’s homology with high confidence. It does not guarantee reconstruction of the manifold itself, so the claim is broader than the result. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The result of this construction is a database M of ca.4.5 × 10 6 points on a 7-dimensional ellipsoid in R 8 .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
What the sources say
- Partly supportedcites [41] lee A.B. Lee, K.S. Pedersen, and D. Mumford, The nonlinear statistics of high-contrast patches in natural images, International Journal of Computer Vision (54), No. 1-3, August 2003, pp. 83-103. sourceExperimental — accuracy not yet measured
Section 3 (Preprocessing; page unavailable) says the preprocessed points lie on a 7-dimensional ellipsoid in R⁹, which is transformed into a 7-dimensional sphere in R⁸. Section 2 (Optical and Range Data Sets; page unavailable) reports about 4.2 million optical patches, not approximately 4.5 million points in a database M. The source supports the general dimensional construction, but not the stated count and geometry together. (LLM, conf=0.85)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Their method is to prove that the Čech complex associated to a covering by balls of a fixed radius is homotopy equivalent to the underlying manifold.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
What the sources say
- Supportedcites [52] Niyogi, Partha, 2008, Finding the homology of submanifolds with high confidence from random samples, Discrete Comput. Geom. source
Section 3 states that, when the sample is sufficiently dense and the ball radius meets the stated bound, the manifold is a deformation retract of the union of the balls. Section 3.1 constructs the nerve of that ball cover and invokes the Nerve Lemma because the balls are convex. Together, these support the claimed homotopy equivalence under the method’s conditions; the paper calls the nerve a simplicial complex rather than a Čech complex. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, it turns out that there is a classification theorem (see [64]) for a subcategory of the category of N-persistence F -vector spaces, where F is a field.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
What the sources say
- Supportedcites [64] Zomorodian, Afra, 2005, Computing persistent homology, Discrete Comput. Geom. source
Section 3.1 (p. 11) establishes an equivalence between finite-type persistence modules and finitely generated graded modules over R[t]. Corollary 3.1 in Section 3.2 (p. 12) then classifies the isomorphism classes of finite-type persistence modules over a field F by finite sets of P-intervals. Finite-type modules are the subcategory referred to in the claim. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In [9], such a context is developed which is not dependent on the choice of a cost function but is rather dependent on”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. source
The Discussion (p. 16) presents persistence and functoriality as organizing principles and proposes defining clustering methods through sets of constraints, rather than a chosen cost function. The Results (p. 10) explicitly compares the framework’s properties with Kleinberg’s axiomatic conditions. (LLM, conf=0.87)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“It can be shown that p Σ is a homotopy equivalence (see [65]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
What the sources say
- Supportedcites [65] Zomorodian, Afra, 2008, Localized homology, Comput. Geom. source
Lemma 1 (global; page unavailable) states that projection from a blowup complex to the covered space is a homotopy equivalence when a simplicial complex is covered by subcomplexes. The paper uses π_X and X_U rather than pΣ and M(|Σ|,S), but states the projection result attributed to it. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This notion of clustering is closely related to clique clustering algorithms in network clustering [53].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [53] clique G. Palla, I. Derènyi, I. Farkas, and T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society, Nature, Volume 435, 9 June 2005, pp. 814-818. source
The paper defines network communities through connected sets of adjacent k-cliques (p. 2) and describes an algorithm that finds cliques and clusters them using a clique-overlap matrix (p. 3). It therefore supports the citation as an example of clique-based network clustering; the comparison to the citing paper’s equivalence relation E is the citing authors’ own framing. (LLM, conf=0.88)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“There is a unique M gen -functorial persistent clustering algorithm Ξ which is scale invariant and which satisfies the requirement that Ξ(E) = P .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Partly supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
- It may state two separate claims as one. 39
- Probably not a substantial claim. 41
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Although clustering is clearly a very important part of data analysis, the ways in which it is formulated and implemented are fraught with ambiguities.In particular, the arbitrariness of various threshhold choices and lack of robustness are difficulties one confronts.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
What the sources say
- Partly supportedcites [43] von Luxburg, Ulrike, 2008, Consistency of spectral clustering, Ann. Statist. sourceExperimental — accuracy not yet measured
The Introduction describes clustering as widely used and says its consistency guarantees are unsatisfactory. The discussion following Proposition 24 shows that unnormalized spectral clustering can fail and that its reliability can be difficult to assess. These passages support the general concern about reliability, but they do not identify arbitrary threshold choices as a difficulty or establish a lack of robustness for clustering generally; no page numbers are available in our copy. (LLM, conf=0.82)
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In particular, in [41], indications were found that the data was concentrated around an annulus.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
What the sources say
- Supportedcites [41] lee A.B. Lee, K.S. Pedersen, and D. Mumford, The nonlinear statistics of high-contrast patches in natural images, International Journal of Computer Vision (54), No. 1-3, August 2003, pp. 83-103. source
Section 5.1 describes the ideal-edge manifold using a circular orientation parameter and a bounded displacement parameter, giving it an annulus-like parametrization. Section 5.2 finds evidence that optical data points concentrate near that manifold. The paper does not use the word “annulus,” but the citing sentence is a reasonable, hedged paraphrase. (LLM, conf=0.82)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“By using persistence, one is able to develop a systematic way of addressing such questions.That methodology has been described in [65].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
What the sources say
- Supportedcites [65] Zomorodian, Afra, 2008, Localized homology, Comput. Geom. source
Section 1.2 describes a method for localizing topological attributes relative to a cover. Section 4.4 gives the persistence algorithm that computes representatives of homology classes, supplying descriptions of those attributes. Together, these passages support the paraphrase about systematically addressing where features come from. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The algorithm Ξ is the algorithm which associates to a finite metric space (X, d X ) the persistent set {π 0 (V R(X, ))} ≥0 .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Supportedcites [9] memolicarlsson G. Carlsson and F. Memoli, Persistent Clustering and a Theorem of J. Kleinberg , Preprint, March 2008. source
Example 2.1 (Section 2, p. 3) associates a finite metric space with a persistent set whose blocks at scale r contain exactly the points connected by chains of distances at most r. Those blocks are the connected components of the Vietoris–Rips complex at that scale, as the claim describes. (LLM, conf=0.90)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Also, results about barcodes under the general heading have now begun to appear (see [15] and [16]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Supportedcites [15] Cohen-Steiner, David, 2007, Stability of persistence diagrams, Discrete Comput. Geom. source
The Introduction (p. 2) says the paper makes concrete statements about the stability of barcodes. The Applications section (p. 16) states a Barcode Theorem, making this paper an example of the barcode results the citing sentence says have begun to appear. (LLM, conf=0.90)
- Not matchedcites [16] stabilitytwo D. Cohen-Steiner, H. Edelsbrunner, J. Harer and Y. Mileyko, Lipschitz functions have 𝐿_{𝑝}-stable persistence, Found. Comput. Math., to appear.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is readily checked that θ is a functor from N pers (Ab) to the category of graded Z[t]-modules, and is in fact an equivalence of categories, since an inverse functor can be given by M * → {M n }, where the morphisms ψ mn are given by multiplication by t n-m .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A first observation is that the points are scattered throughout the 7-sphere, in the sense that no point on S 7 is very far from the set, but that the density appears to vary a great deal.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that there are a number of short lines, and one long one.According to the philosophy mentioned above, this suggests that the first Betti number should be estimated to be one.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedgedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that in this case, there are many short segments and 5 longer segments.It is again the case that this barcode is stable over the choices of landmark points and samples from M. This suggests that the first Betti number of the putative underlying space should be five.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Primary circle
hedgedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that while the two smaller holes have now been “filled in”, a new hole has been introduced in the lower right-hand portion of the figure.Consequently, if we were to compute the homology of this complex, we would obtain a first Betti number of two.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We note that the vertical circles intersect the horizontal primary circle in two points, and do not intersect each other, so the picture in question produces a natural embedding of the three circle model in the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that the β 0 barcode (the upper picture) clearly shows the single component, the β 1 shows two lines from threshhold parameter values .15 to .35, and finally the β 2 barcode shows a single line on roughly the same interval.This gives β 0 = 1, β i = 2, and β 2 = 1.These are the mod 2 Betti numbers for the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At both scales, there is a central dense core, and two”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These two are distinguished by mod 3 homology, and we have performed the computation to show that the mod 3 homology is consistent with the Klein bottle and not with the torus.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that given only the Mapper output, one might suspect that the small feature (the array of orange nodes) could simply be an artifact, but examination of the data shows that they correspond to essentially distinct contact maps.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Some experimentation with the three circle model results in the following picture in which the horizontal segments form the primary circle and the vertical segments form the secondary circles, as in the diagram.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We have informally confirmed that the indicated patches are the ones which occur in the high density portions of the data set.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By far the most frequently occurring signatures were (1, 1, 0) and (1, 0, 1), corresponding to a circle and a sphere, respectively.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that the two secondary circles each intersect the primary circle in two points, and that they do not intersect each other.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note also that the data are obtained by simply examining the states occurring in the simulation, and that it does not include any dynamic information which would show how the states are traversed in the folding process.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The set S exhibits the three circle model clearly, but enlarging it still does not exhibit the non-trivial β 2 which would be characteristic of the Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is easy to check (a) that the factorization is a homeomorphism and (b) that the orbit space is homeomorphic to a Klein bottle.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Three circle model in the data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A M iso -functorial clustering algorithm determines an choice p ι ∈ P G ι ι for every ι, and conversely an arbitrary choice of such p ι 's determines an M iso -functorial clustering algorithm.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Each of these notions creates the structure of a category whose objects are the finite metric spaces, since one can readily observe that each of the classes of morphisms is closed under composition and contains the identity.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Some work in clustering theory has been done in trying to determine the optimal choice of , but it is now well understood that it is much more informative to maintain the entire dendrogram of the set, which provides a summary of the behavior of clustering under all possible values of the parameter at once.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the biological context, notions of distance are constructed using some intuitively attractive measures of similarity (such as BLAST scores or their relatives), but it is far from clear how much significance to attach to the actual distances, particularly at large scales.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Topology studies only properties of geometric objects which do not depend on the chosen coordinates, but rather on intrinsic geometric properties of the objects.As such, it is coordinate-free.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Moreover, it is understood that most of the information about topological spaces can be obtained through diagrams of discrete sets, via a process of simplicial approximation.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then demonstrate that persistence can be generalized in several different directions, providing more structure and information about the data sets in question.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the clusterings are consistent, i.e. if the clusters in C(X 1 ) and C(X 2 ) in C(X 1 ∪ X 2 ) correspond well under these maps, then one can regard that as evidence that the subsample clusterings actually correspond to clusterings on the full data set X.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
hedgedcorrelationaln = 2- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For any topological space X with a finite number of path components, β 0 (X) is the number of path components.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We begin with a discussion of persistent homology, which is a mathematical formalism which permits us to infer topological information from a sample of a geometric object, and we show how it can be applied to particular data sets arising from natural image statistics and neuroscience.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Functoriality is central in algebraic topology in that the functoriality of homological invariants is what permits one to compute them from local information, and that functoriality is at the heart of most of the interesting applications within mathematics.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then show that the philosophy of functoriality can be used to reason about the nature of clustering methods, and we conclude by speculating about theorems one might hope to prove and discussing how the subject might develop more generally.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“One important goal of data analysis is to allow the user to obtain knowledge about the data, i.e. to understand how it is organized on a large scale.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although we often receive data in the form of vectors of real numbers, it is frequently the case that the coordinates, like the metrics mentioned above, are not natural in any sense, and that therefore we should not restrict ourselves to studying properties of the data which depend on any particular choice of coordinates.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The second method is more efficient, since it is precisely adapted to the complex at hand.Furthermore, it contains complete information about the original R-vector space.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In interpreting the output, one now finds that long intervals in the output barcode indicate the presence of a homology class which “persists” over a long range of parameter values, while short intervals indicate cycles which are “born” at a given parameter value and then “die” at a nearby parameter value.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is due to the fact that for finite metric spaces, it is generically the case that each value of the distance is taken only for one pair of points, so one does not have any points which are equidistant between a pair of landmarks.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Intuitively, we regard these two holes as coming from faulty sampling or other errors in the recovery of the data.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=3
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The pictures and discussion above allow us to formulate the intuition that long intervals correspond to large scale geometric features in the space and that short intervals correspond to noise or inadequate sampling.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 1=2
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Both the the Čech and Vietoris-Rips complexes typically produce simplices in dimensions much higher than the dimension of the space.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It turns out that H simp k (X, Z) is always canonically isomorphic to the singular homology of the space |X|.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Remark.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The conclusion is that for simplicial complexes, homology is algorithmically computable, although standard methods for computing Smith normal form can often be painfully slow.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Remark.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If further is sufficiently small, then all the balls will be geodesically convex, and the complex will compute the homology of M correctly.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that if the centers actually lie on a submanifold M ⊆ R n , and the set X is sufficiently dense in M , then this complex is the Čech complex attached to a covering of M .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.3. Persistent homology.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is clearly the case that if we have 0 ≤ ≤ , then we have an inclusion W s (X, L, ) ↩→ W s (X, L, ) and similarly for W s V R , W w , and W w V R .”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, for finite metric spaces, it generically produces degenerate (i.e.discrete) complexes, with no one dimensional simplices.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.2. Building coverings and complexes.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In rough terms, δ k for large values of k computes density using points in large neighborhoods of x, and for small values uses small neighborhoods.So, δ k for large k corresponds to a smoothed out notion of density, and for small k corresponds to a version which carries more of the detailed structure of the data set.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.4. Example: Natural image statistics.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“It was observed in [38] that in an informal sense, the images in the different conditions appeared to be quite similar, and a statistical analysis strongly suggested that the behavior of V1 in the spontaneous condition was consistent with a behavior which consisted in moving through a family of evoked images corresponding to responses to angular boundaries, without any particular order.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
What the sources say
- Not checked — no full text of the sourcecites [38] kenet T. Kenet, D. Bibitchkov, M. Tsodyks, A. Grinvald, and A. Arieli, Spontaneously emerging cortical representations of visual attributes, Nature 425 (2003), 954-956. source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This result is consistent with what was obtained by using ISOMAP in [61].”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
What the sources say
- Not checked — no full text of the sourcecites [61] isomapJ.B. Tenenbaum, V. de Silva and J.C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science 290 (2000) (December), pp. 2319-2323. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The arrays of neurons studied in [59] are from the primary visual cortex or V1 in Macaque monkeys.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 2.5. Example: Electrode array data from primary visual cortex.
What the sources say
- Not checked — no full text of the sourcecites [59] visionG. Singh, F. Memoli, T. Ishkhanov, G. Carlsson, G. Sapiro and D. Ringach, Topological Structure of Population Activity in Primary Visual Cortex, Journal of Vision, Volume 8, Number 8, Article 11, pp. 1-18, 2008. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“since the intuition is that features which appear at several levels in such a multiresolution diagram would be more intrinsic to the data set than those which appear at a single level.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 3.2.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The core consists of normal or near-normal patients, and the two flares consist of patients with the two different forms of diabetes.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 3.5. Examples.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Z[m, n]-persistence vector spaces are examples of quiver representations, a highly developed area of algebra.See [29] for a complete description.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Corollary 4.6.
What the sources say
- Not checked — no full text of the sourcecites [29] Gabriel, P., 1997 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These functions are clearly invariants of the isomorphism class of the module M , and we refer to them as the dimension and rank invariants, respectively.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 4.3.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the question is instead whether or not an element arises from a union of k elements of S, then we do not obtain complete information this way, but we do obtain partial information in that we can preclude the possibility that a class arises from such a union.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“We note that the correspondence X → π 0 (X) can actually be viewed as a functor (see [44]) from the category of topological spaces to the category of sets, in the sense that a continuous map f : X → Y induces a map of sets π 0 (f ) : π 0 (X) → π 0 (Y )”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not matchedcites [44] Mac Lane, Saunders, 1998
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As we have seen above, algebraic topology is capable of producing signatures which indicate the presence of topological features within a space.As it stands, however, it is not capable of describing the source of the feature, i.e.where in the space the hole or other feature is located.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Theorem 4.5.Let M denote any Z[m, n]-persistent F -vector space, so that every vector space V i is finite dimensional.Then there is an isomorphism”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Example.
What the sources say
- Not checked — no full text of the sourcecites [29] Gabriel, P., 1997 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“As Kleinberg points out, it is possible to do so in a number of different ways by specifying cost functions on particular clusterings, and prove a uniqueness result for optimal choices of clusterings with respect to this cost function”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not matchedcites [39] kleinbergJ.M. Kleinberg, An impossibility theorem for clustering, NIPS 2002: 446-453.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“This machinery is not available in the multivariable case, but it turns out that it can be replaced by the Gröbner basis methodology (see [18] or [47]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Definition 4.3.
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Any finite Z-persistence F -vector space M can be decomposed as”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Corollary 4.6.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The properties of trees guarantee that ≤ T is a partial ordering.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 4.3. Tree based persistence.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“In the paper [59], this was shown by simulation, which is of course always an option.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
What the sources say
- Not checked — no full text of the sourcecites [59] visionG. Singh, F. Memoli, T. Ishkhanov, G. Carlsson, G. Sapiro and D. Ringach, Topological Structure of Population Activity in Primary Visual Cortex, Journal of Vision, Volume 8, Number 8, Article 11, pp. 1-18, 2008. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Not only is the correspondence π 0 (-) a functor from spaces to sets, it is equipped with a natural surjective map of sets η X : X → π 0 (X).”
- Also stated in: § 5. Reasoning about clustering · plain
“Further, these surjective maps have the property that for every continuous map f : X → Y , the diagram of sets (below) commutes.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Also stated in: § 5. Reasoning about clustering · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While the observation is of course suggestive, to prove that it indicates structure in the data set one must show that segments of the indicated length cannot occur in a random model associated to a null hypothesis.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means that Kleinberg's conditions also make sense in this context.”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“We point out that in the context of ordinary homology (i.e.not persistent homology), M. Penrose and others have developed a theory of “geometric random graphs” and have proved various results concerning the Betti numbers of complexes attached to such graphs (see [54]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Not checked — no full text of the sourcecites [54] Penrose, Mathew, 2003 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is clear that a C-functorial clustering algorithm is a clustering algorithm in the sense of Kleinberg, since the surjective map of sets from X to χ(X, d X ) yields a partition of X, namely the partition whose blocks are the sets η -1 X (z), as z ranges over all elements of χ(X, d X ).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > 5. Reasoning about clustering
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Each such interval can be considered as a point in the set D = {(x, y) ∈ R 2 |x ≤ y}, and the output is therefore a finite subset of the set D. As such, we now have a finite spatial point process (see [20], [3]).”
§ TOPOLOGY AND DATA GUNNAR CARLSSON > Additional information
What the sources say
- Not matchedcites [20] Daley, D. J., 1988
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [3] Baddeley, Adrian, 2007, Spatial point processes and their applications source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper relies on this without support.
There is no classification theorem for R-persistence abelian groups.
“However, we do not have such a theorem.”
The paper relies on this without support.
There is still no classification theorem for graded Z[t]-modules.
“Now, there is still no classification theorem for graded Z[t]-modules.”
The paper relies on this without support.
In studying data from experiments, one typically cannot assume that the data lies along a submanifold.
“If one is interested in studying data from experiments, though, one typically cannot assume that the data lies along a submanifold.”
The paper relies on this without support.
Even if one could assume that the data lies along a manifold, one is usually not in a position to verify that the stringent hypotheses of [52] are satisfied.
“Further, even if one could assume that the data lies along a manifold, one is usually not in a position to verify that the stringent hypotheses of [52] are satisfied.”
The paper relies on this without support.
The analysis of populations of neurons is amenable to geometric analysis.
“The second problem appears to be very amenable to geometric analysis, since it will involve the activities of several neurons at once.”
The paper relies on this without support.
The mechanism by which V1 carries out low-level tasks such as edge and line detection is not understood.
“However, the mechanism by which it carries out these tasks is not understood.”
The paper relies on this without support.
The classification of finitely generated modules over multivariable polynomial rings is much more complicated than the corresponding result for single-variable polynomial rings, and no reasonable parametrization is known.
“As is well known to algebraists, the classification of finitely generated modules over multivariable polynomial rings is much more complicated than the corresponding result for single variable polynomial rings, and in fact no reasonable parametrization is known.”
The paper relies on this without support.
In the case of more than one variable, the classification of multigraded modules depends on the field in question.
“examples show that in the case of more than one variable, the classification of multigraded modules definitely depends on the field in question.”
The paper relies on this without support.
The classification in the multivariable case is parametrized by points in moduli varieties over the ground field.
“In fact, the classification in the multivariable case is parametrized by points in moduli varieties over the ground field”
The paper relies on this without support.
Hierarchical clustering algorithms do not report single partitions but rather dendrograms, which are roughly speaking R+-persistent sets.
“This is not an unreasonable thing to do in view of the fact that hierarchical clustering algorithms do not report single partitions but rather dendrograms, which are roughly speaking R + -persistent sets.”
Citations
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Cited by
Sorted by citations
Items 801–900 of 2,331
Lee, M. P., Kim, D. W., Mayer, C., Walch, O., & Forger, D. B. (2024) · Journal of Biological Rhythms · doi:10.1177/07487304241288607 · Cited by 10
“One promising tool for this is topological data analysis (TDA), a data analysis framework exploiting theories in algebraic topology (Edelsbrunner et al., 2002; Carlsson, 2009; Epstein et al., 2011; Edelsbrunner and Harer, 2022; Skaf and Laubenbacher, 2022).”
Mathers, A. J., Li, T. J. X., He, Q., et al. (2024) · npj Antimicrobials and Resistance · doi:10.1038/s44259-024-00069-w · Cited by 10
“Our CSC model employs simplicial complexes, which extend the concepts of networks and hypergraphs to model higher-order relationships in real-world data sets 36 .”
Non locality, Topology, Formal Languages: New Global Tools to Handle Large Data Sets
Merelli, E., & Rasetti, M. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.05.172 · Cited by 10
“We believe that one way to create such models is that of extracting them from the data by which the complex system itself is characterized.”
BackgroundMesh generation and geometric persistent homology
Miller, G. L., & Sheehy, D. R. (2011) · Cited by 10
Distances and isomorphism between networks: stability and convergence of network invariants
Chowdhury, S., & Mémoli, F. (2022) · Journal of Applied and Computational Topology · doi:10.1007/s41468-022-00105-6 · Cited by 9
Neighborhood Path Complex for the Quantitative Analysis of the Structure and Stability of Carboranes
Liu, J., Chen, D., Pan, F., et al. (2023) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416523500229 · Cited by 10
Topological Signal Processing Over Weighted Simplicial Complexes
Battiloro, C., Sardellitti, S., Barbarossa, S., et al. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094927 · Cited by 10
“In the last years, there has been a growing interest in the processing of signals defined over topological spaces [1], [2], i.”
BackgroundChatGPT for computational topology
Liu, J., Shen, L., & Wei, G. (2024) · Foundations of Data Science · doi:10.3934/fods.2024009 · Cited by 11
“An increasing number of scholars and researchers are dedicating themselves to develop the theories on TDA [Car09, CZCG04, ELZ02, ZC04].”
BackgroundSome Remarks on the Algebraic Properties of Group Invariant Operators in Persistent Homology
Frosini, P., & Quercioli, N. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66808-6_2 · Cited by 10
Metric thickenings of Euclidean submanifolds
Adams, H., & Mirth, J. (2018) · Topology and its Applications · doi:10.1016/j.topol.2018.12.014 · Cited by 10
“In applications of topology to data analysis [8, 13] the datasets will typically be finite, but nevertheless infinite Vietoris–Rips constructions are important for applications in part because if a dataset Y converges to an infinite shape X in the Gromov–Hausdorff distance, then the persistent homology of VR(Y ; r) co…”
BackgroundDynamic sensor fusion using local topology
Robinson, M., Henrich, J., Capraro, C., et al. (2018) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero.2018.8396546 · Cited by 10
“Topological data analysis [5-8] methods focus on the topological schema itself, which is a fruitful place to begin analysis.”
MethodLoop homology of bi-secondary structures
Bura, A. C., He, Q., & Reidys, C. M. (2021) · Discrete Mathematics · doi:10.1016/j.disc.2021.112371 · Cited by 10
“This analysis represents a version of persistent homology, pioneered by Edelsbrunner and by Gunnar Carlson [46, 47, 48] and is of central importance for designing an optimal loop-removal schedule in [41].”
MethodTopology-Aware Single-Image 3D Shape Reconstruction
Chen, Q., Nguyen, V., Han, F. S., Kiveris, R., & Tu, Z. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00143 · Cited by 10
“Geometric and topological data analysis [5, 7] has inspired abundant methods to be developed in various directions and applications.”
BackgroundSmooth Summaries of Persistence Diagrams and Texture Classification
Chung, Y.-M., Hull, M., & Lawson, A. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00428 · Cited by 10
“Topological Data Analysis (TDA) is a field of research lying at the intersection of mathematics, statistics, and computer science that is concerned with understanding data through its shape (see survey articles and references therein [8; 9; 21; 42; 13]).”
BackgroundTDAview: an online visualization tool for topological data analysis
Walsh, K., Voineagu, M., Vafaee, F., & Voineagu, I. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa600 · Cited by 10
Terebus, A., Manuchehrfar, F., Cao, Y., & Liang, J. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.645640 · Cited by 10
“Persistent homology provides a powerful method that can characterize topological features of high dimensional probability landscapes (Edelsbrunner et al., 2002; Carlsson, 2009).”
MethodPersistent homology of fractional Gaussian noise
Masoomy, H., Askari, B., Najafi, M. N., & Movahed, M. S. (2021) · Physical review. E · doi:10.1103/physreve.104.034116 · Cited by 10
A Robust Topology-Based Algorithm for Gene Expression Profiling
Seemann, L., Shulman, J., & Gunaratne, G. H. (2012) · ISRN Bioinformatics · doi:10.5402/2012/381023 · Cited by 10
“A generalization of this statement forms the novel proposal in [13, 14], which associates robust characteristics of the point cloud with “persistent” topological invariants.”
BackgroundZinc Finger Proteins and the 3D Organization of Chromosomes
Feinauer, C., Hofmann, A., Goldt, S., Liu, L., Máté, G., & Heermann, D. W. (2013) · Advances in protein chemistry and structural biology · doi:10.1016/b978-0-12-410523-2.00003-1 · Cited by 10
Texture recognition by the methods of topological data analysis
Makarenko, N. G., Kalimoldayev, M., Pak, I., et al. (2016) · Open Engineering · doi:10.1515/eng-2016-0044 · Cited by 10
“The third direction forms a TDA, Topological Data Analysis is a new area of intelligent processing of experimental information [31, 32].”
BackgroundThe Many Dimensions of Diastolic Function
Sengupta, P. P., & Marwick, T. H. (2017) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2017.05.015 · Cited by 10
“Because large numbers of measurements are made on the same “state,” there is an inherent interconnectedness and a unique “structure” to these observations (5,6).”
BackgroundWhen remote sensing meets topological data analysis
Duponchel, L. (2018) · Journal of Spectral Imaging · doi:10.1255/jsi.2018.a1 · Cited by 10
ROLE OF LOCAL GEOMETRY IN ROBUSTNESS OF POWER GRID NETWORKS
Islambekov, U., Dey, A. K., Gel, Y. R., et al. (2018) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2018.8646573 · Cited by 10
“For instance, the Vietoris-Rips (VR) complex is one of the most popular choices in TDA due to its simplicity and computational advantages [14, 15].”
BackgroundMethodBlevins, A. S., & Bassett, D. S. (2020) · Springer eBooks · doi:10.1007/978-3-319-70658-0_87-1 · Cited by 9
Impact-induced hardening in dense frictional suspensions
Pradipto, & Hayakawa, H. (2021) · Institutional Repositories DataBase (IRDB) · Cited by 10
Čech Type Approach to Computing Homology of Maps
Mrożek, M. (2010) · Discrete & Computational Geometry · doi:10.1007/s00454-010-9255-2 · Cited by 10
“For instance, the computations on interval [0,1] yield (3 ∗ [0,1] − 8) ∗ (5 ∗ [0,1] + 8)/60 = [−8,−5] ∗ [8,13]/60 = [−104,−40]/60 = [−2,0], therefore F([0,1])= [−2,0] ⊂ Y , which explains the need of considering the Čech structure Y .”
BackgroundPrincipled Simplicial Neural Networks for Trajectory PredictionPreprint
Roddenberry, T. M., Glaze, N., & Segarra, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.10058 · Cited by 48
“We can understand data supported on simplicial complexes using tools from algebraic topology (Hatcher, 2002; Carlsson, 2009; Ghrist, 2014); in particular, we can analyze data supported on the edges and higher-order structures using the spectrum of certain linear operators on the simplicial complex.”
BackgroundMethodJiao, Y., Shen, G., Lin, Y., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.06708 · Cited by 10
Dynamical models for random simplicial complexes
Fountoulakis, N., Iyer, T., Mailler, C., & Sulzbach, H. (2022) · The Annals of Applied Probability · doi:10.1214/21-aap1752 · Cited by 9
“Additionally, (random) simplicial complexes have already been used in applications such as topological data analysis (see, for example, [19]), and recent theories of quantum gravity (see, for example, [1]).”
MethodAugmenting Molecular Deep Generative Models with Topological Data Analysis Representations
Schiff, Y., Chenthamarakshan, V., Hoffman, S. C., Ramamurthy, K. N., & Das, P. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747088 · Cited by 9
“At the same time, TDA representations are a natural fit for molecular generative models for several other reasons: They incorporate global topological information [2], which cannot be captured by molecular graphs or point clouds.”
BackgroundEvolutionary Khovanov homology
Shen, L., Liu, J., & Wei, G. (2024) · AIMS Mathematics · doi:10.3934/math.20241277 · Cited by 9
“Persistent homology, a powerful method from algebraic topology, facilities TDA and has been used to capture the topological features of point cloud data by continuously analyzing its topological structures at different scales [14, 15].”
Suwayyid, F., & Wei, G. (2024) · Journal of Physics Complexity · doi:10.1088/2632-072x/ad83a5 · Cited by 9
“The initial success of TDA is mainly due to persistent homology [16, 28, 29, 67], a technique integrating topology and multiscale analysis.”
MethodCheng, M., Jansen, J., Reimer, K. C., Grande, V. P., Nagai, J. S., Li, Z., Kießling, P., Graßhoff, M., Kuppe, C., Schaub, M. T., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02870-5 · Cited by 10
“The filtering of edges by distance to obtain the triangulated graph is related to PH analysis 35 .”
InfluentialOn the Topological Data Analysis extensions and comparisons
Noreldeen, A. H., & Mohamed, A. H. N. (2017) · Journal of the Egyptian Mathematical Society · doi:10.1016/j.joems.2017.07.001 · Cited by 9
On a New Method to Build Group Equivariant Operators by Means of Permutants
Camporesi, F., Frosini, P., & Quercioli, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99740-7_18 · Cited by 9
“In particular, topological data analysis (TDA) has revealed important in managing the huge amount of data that surrounds us in the most varied contexts [3].”
BackgroundProbing the geometry of data with diffusion Fréchet functions
Martínez, D. H. D., Lee, C. H., Kim, P. T., et al. (2018) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2018.01.003 · Cited by 9
An economic topology of the Brexit vote
Rudkin, S., Barros, L., Dłotko, P., et al. (2023) · Regional Studies · doi:10.1080/00343404.2023.2204123 · Cited by 9
“ithin the combined categories. Topological data analysis (TDA) is a data-driven approach from the physical sciences that treats data as a point cloud and studies the topology thereof. Born of work by Carlsson (2009) TDA is well adopted in the physical sciences but is yet to take hold in the social sciences. It is free…”
BackgroundTopological features of multivariate distributions: Dependency on the covariance matrix
Aromi, L. L., Katz, Y. A., & Vives, J. (2021) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2021.105996 · Cited by 9
Path homologies of motifs and temporal network representations
Chowdhury, S., Huntsman, S., & Yutin, M. (2022) · Applied Network Science · doi:10.1007/s41109-021-00441-z · Cited by 11
“structures (Carlsson 2009).”
Persistent Homology: A Topological Tool for Higher-Interaction Systems
Vaccarino, F., Fugacci, U., & Scaramuccia, S. (2022) · Understanding complex systems · doi:10.1007/978-3-030-91374-8_3 · Cited by 9
Persistent homology classification algorithm
Lara, M. L. D. D. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1195 · Cited by 11
“A training dataset is composed of ( n þ 1)-dimensional data points which are split into an n -dimensional input vector often that persist across multiple resolutions ( Edelsbrunner & Harer, 2008 ; Carlsson, 2009 ; Edelsbrunner & Harer, 2010 ).”
Persistent Topological Laplacian Analysis of SARS-CoV-2 Variants
Wei, X., Chen, J., & Wei, G. (2023) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416523500278 · Cited by 9
“Through analyzing the signature and change of topological invariants during filtration, one can infer the shape of data [5].”
Rasetti, M., & Merelli, E. (2015) · Journal of Physics Conference Series · doi:10.1088/1742-6596/626/1/012005 · Cited by 8
“The basic principles of the approach stem out of the seminal work of a number of authors: G. Carlsson [8], H. Edelsbrunner and J. Harer [9], A.J. Zomorodian [10], and others.”
BackgroundChen, S., Wu, X., & Yin, H.-F. (2016) · Pattern Analysis and Applications · doi:10.1007/s10044-016-0531-5 · Cited by 9
“Maybe we can solve this problem with the help of the topology literature [32].”
MethodData-Driven Topological Motion Planning with Persistent Cohomology
Pokorny, F. T., & Kragić, D. (2015) · doi:10.15607/rss.2015.xi.049 · Cited by 9
“In future work, we intend to investigate the combination of our methods with probabilistic and cost based filtrations as well as the use of alternatives to the underlying Delaunay-Čech complexes.”
MethodHahm, J., Lee, H., Park, H., Kang, E., Kim, Y. K., Chung, C. K., Kang, H., & Lee, D. S. (2017) · Scientific Reports · doi:10.1038/srep41592 · Cited by 9
Computing all maps into a sphere
Čadek, M., Krčál, M., Matoušek, J., Sergeraert, F., Vokřínek, L., & Wagner, U. (2012) · ACM-SIAM Symposium on Discrete Algorithms (SODA) · doi:10.1137/1.9781611973099.1 · Cited by 9
Topological Characteristics of Oil and Gas Reservoirs and Their Applications
Байков, В. А., Gilmanov, R., Taimanov, I. A., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69775-8_11 · Cited by 9
“Since the “permeability” function Z determines a natural filtration of the reservoir by the excursion sets it is reasonable to pick up the topological picture of the filtration and use for that the persistent homology [2, 3] (see also [4, 5, 6, 7]).”
BackgroundGeneralized cluster trees and singular measures
Chen, Y. (2019) · The Annals of Statistics · doi:10.1214/18-aos1744 · Cited by 9
“The cluster tree is also related to the topological data analysis (Carlsson, 2009; Edelsbrunner and Morozov, 2012; Wasserman, 2016).”
A topological study of functional data and Fréchet functions of metric measure spaces
Hang, H., Mémoli, F., & Mio, W. (2019) · Journal of Applied and Computational Topology · doi:10.1007/s41468-019-00037-8 · Cited by 9
“In defining persistent homology (Frosini 1992; Robins 1999; Edelsbrunner et al. 2002; Carlsson 2009; Edelsbrunner and Harer 2008; Cohen-Steiner et al. 2007) invariants for X f , there are a few drawbacks in following the standard procedure of using the filtration of X given by the sublevel sets of…”
BackgroundMethodInfluentialDiscovery of new microscopic structures in surface water on graphene using data science
Kato, K., Maekawa, Y., Watanabe, N., Sasaoka, K., & Yamamoto, T. (2019) · Japanese Journal of Applied Physics · doi:10.7567/1347-4065/ab6564 · Cited by 9
TopoBARTMAP: Biclustering ARTMAP with or without Topological Methods in a Blood Cancer Case Study
Yelugam, R., da Silva, L. E. B., & Wunsch, D. C. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206684 · Cited by 8
“In an attempt to mitigate this problem, topological data analysis is becoming popular for gene expression data [8], [9].”
BackgroundTopological data analysis for friction modeling
Hasan, M. S., & Nosonovsky, M. (2021) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/ac2655 · Cited by 9
A (simple) classical algorithm for estimating Betti numbers
Apers, S., Gribling, S., Sen, S., et al. (2023) · Quantum · doi:10.22331/q-2023-12-06-1202 · Cited by 9
“There is a recent surge of interest in using simplicial complexes to model higher-order relations in data sets – a technique often referred to as “ topological data analysis ” [6].”
BackgroundPersistent extensions and analogous bars: data-induced relations between persistence barcodes
Yoon, H. R., Ghrist, R., & Giusti, C. (2023) · Journal of Applied and Computational Topology · doi:10.1007/s41468-023-00115-y · Cited by 14
Fan, N., & Golari, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12691-3_46 · Cited by 9
“(a) It can be interpreted as the zeroth Betti number of the graph G, in topological theory (see [1]).”
BackgroundPersistent topology of decision boundaries
Varshney, K. R., & Ramamurthy, K. N. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178708 · Cited by 9
“Index Terms— Graph walk, persistent homology, simplicial complex, supervised classification, topological data analysis”
BackgroundMethodSpectral sequences, exact couples and persistent homology of filtrations
Basu, S., & Parida, L. (2016) · Expositiones Mathematicae · doi:10.1016/j.exmath.2016.06.007 · Cited by 9
“rvals often carry important information about the underlying data sets that give rise to the filtration, and this is why computing them is important in practice. We refer the reader to survey articles [4],[9],[14] for details regarding these applications. While spectral sequences and persistent homology were invented f…”
BackgroundjavaPlex: a research platform for persistent homology
Tausz, A., Vejdemo‐Johansson, M., & Adams, H. (2012) · Cited by 9
Higher Interpolation and Extension for Persistence Modules
Bubenik, P., de Silva, V., & Nanda, V. (2017) · SIAM Journal on Applied Algebra and Geometry · doi:10.1137/16m1100472 · Cited by 9
“The combination of rigorously developed foundations [6, 10], efficient computability [21, 25], and stability properties [8, 11] has resulted in the widespread adoption of topological persistence [15, 25] as a technique for the analysis of large and complex datasets [7, 16, 22].”
BackgroundUnderstanding Crime with Computational Topology
Brantingham, P. L., & Brantingham, P. J. (2015) · Palgrave Macmillan UK eBooks · doi:10.1057/9781137391322_10 · Cited by 9
Geometry and topology of the space of sonar target echos
Robinson, M., Fennell, S., DiZio, B., & Dumiak, J. (2018) · The Journal of the Acoustical Society of America · doi:10.1121/1.5027825 · Cited by 10
Convergence of combinatorial gravity
Kelly, C., Biancalana, F., & Trugenberger, C. A. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.105.124002 · Cited by 12
“topological data analysis [22]—network geometry [17, 19, 59] and quantum gravity.”
BackgroundTopological approximate Bayesian computation for parameter inference of an angiogenesis model
Thorne, T., Kirk, P., & Harrington, H. A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac118 · Cited by 9
“The computation crucially depends on the choice of filtration, which is a nested sequence of spaces built on the data, that is indexed by a scale parameter (Edelsbrunner and Harer, 2010; Ghrist, 2018).”
3D shape-dependent thinning method for trabecular bone characterization
Jennane, R., Almhdie-Imjabbar, A., Aufort, G., et al. (2011) · Medical Physics · doi:10.1118/1.3664005 · Cited by 9
Modeling Collaborations with Persistent HomologyPreprint
Bampasidou, M., & Gentimis, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.5346 · Cited by 9
“nts are homogenous. We do not require that to be the case. We actually base our analysis on the dierences amongst them. We apply techniques from a new but very promising eld of Computational Topology[1]. Persistent homology is used in an attempt to extract information from high-dimensional data sets recognising shapes…”
MethodMulticore Homology via Mayer VietorisPreprint
Lewis, R. H., & Zomorodian, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.2275 · Cited by 9
“This is not surprising since for any p ∈ [2 , 10] this Preliminary Draft — 7/10/14 — Page 9 7: Statistics for partitions and covers generated complex exhibits a partition-based cover which balances its 46.5M simplices nearly perfectly while maintaining that the size of all intersections between all…”
BackgroundRandom geometric complexes in the thermodynamic regimePreprint
Yogeshwaran, D., Subag, E., & Adler, R. J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.1164 · Cited by 9
“rest in random geometric complexes was stimulated by their connections to topological data analysis and, more broadly, applied topology. There are a number of accessible surveys on this subject (e.g. [6, 9, 12, 15, 45]), all of which share a common theme of studying topological invariants of simplicial complexes built…”
BackgroundCrackle: The Persistent Homology of NoisePreprint
Adler, R. J., Bobrowski, O., & Weinberger, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.1466 · Cited by 8
“[6,8–10]).”
Quantifying topological invariants of neuronal morphologiesPreprint
Kanari, L., Dłotko, P., Scolamiero, M., Levi, R., Shillcock, J. C., Hess, K. P., & Markram, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.08432 · Cited by 9
“In general, the properties of geometric trees, described by a set of points, have been rigorously studied in two extreme cases: in the limit of the full complexity of the trees [17], where the entire set of points is used, and in the opposite limit of a feature space [10, 18], where a (typically small) number of selec…”
BackgroundMethodInfluentialSimBa: An Efficient Tool for Approximating Rips-Filtration Persistence via Simplicial Batch-CollapsePreprint
Dey, T. K., Shi, D., & Wang, Y. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.esa.2016.35 · Cited by 25
Persistent homology: a tool to universally measure plant morphologies across organs and scalesPreprint
Li, M., Frank, M. H., Coneva, V., Mio, W., Topp, C. N., & Chitwood, D. H. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/104141 · Cited by 9
“Persistent homology is a topological data analysis method that can be used to 127 quantify complex shapes and construct informative summaries of data (Verri et al., 128 1993; Carlsson, 2009; Edelsbrunner and Harer, 2010).”
BackgroundMethodCrawford, L. A., Monod, A., Chen, A., Mukherjee, S., & Rabadán, R. (2016) · arXiv (Cornell University) · Cited by 8
Moosavi, V. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.02939 · Cited by 13
Computing Wasserstein Distance for Persistence Diagrams on a Quantum ComputerPreprint
Berwald, J., Gottlieb, J. M., & Munch, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.06433 · Cited by 28
Computing Topological Persistence for Simplicial MapsPreprint
Dey, T. K., Fan, F., & Wang, Y. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1208.5018 · Cited by 9
“While the idea of maintaining a cohomology basis through an inclusion is not new, maintaining them through a vertex collapse is new, which constitutes an important atomic operation for simulating simplicial maps.”
BackgroundLimit theorems for persistence diagramsPreprint
Duy, T. K., Hiraoka, Y., & Shirai, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.08371 · Cited by 9
“at ltration. The persistent homology [9,34] is a tool invented for this purpose, and especially, its expression called persistence diagram is now applied to a wide variety of applied areas (see e.g., [3,10,13,24,31]). From this point of view, there have been some works on a functional of persitence diagram, called lif…”
BackgroundChung, Y.-M., Hu, C.-S., Lo, Y., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.06856 · Cited by 9
“Topological data analysis (TDA) is a data analysis framework based on tools from algebraic topology (Carlsson, 2009; Epstein et al., 2011).”
MethodThe growing topology of the C. elegans connectomePreprint
Helm, A., Blevins, A. S., & Bassett, D. S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.31.424985 · Cited by 8
“In order to detect voids within the growing network, we use persistent homology [13, 14, 15], which records the voids, also known as cavities, that emerge, persist, and collapse throughout the course of the growing connectome’s development.”
MethodÇınelı, E., Ginzburg, V. L., & Gürel, B. Z. (2024) · Mathematische Zeitschrift · doi:10.1007/s00209-024-03627-0 · Cited by 32
“(See, e.g., [24] and also [90] for an introduction to persistent homology theory and further references).”
Topology recapitulates morphogenesis of neuronal dendrites
Liao, M., Bird, A. D., Cuntz, H., & Howard, J. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113268 · Cited by 8
Persistent homology reveals strong phylogenetic signal in 3D protein structures
Dagher, L. B., Madern, D., Malbos, P., & Brochier‐Armanet, C. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae158 · Cited by 8
Acute lymphoblastic leukemia classification using persistent homology
Shah, W. H., Baloch, A., Jaimes-Reátegui, R., Iqbal, S., Fatima, S. R., & Pisarchik, A. N. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01301-4 · Cited by 9
Relational Persistent Homology for Multispecies Data with Application to the Tumor Microenvironment
Stolz, B. J., Dhesi, J., Bull, J. A., Harrington, H. A., Byrne, H. M., & Yoon, I. H. R. (2024) · Bulletin of Mathematical Biology · doi:10.1007/s11538-024-01353-6 · Cited by 10
The Chronic Heart Failure Evolutions: Different Fates and Routes
Agostoni, P., Chiesa, M., Salvioni, E., et al. (2024) · ESC Heart Failure · doi:10.1002/ehf2.14966 · Cited by 8
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentation
Xie, R., Jiang, L., He, X., et al. (2024) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme57554.2024.10687838 · Cited by 8
“To visually explore and explain global rules within the learned class-related manifold, we further perform topological data analysis (TDA) [28] on these CS codes, which presents significant connections between samples and potential structural patterns of the data through a topological…”
Methoddos Santos, K. R. M., Rezaie, A. H., & Beyer, K. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.119088 · Cited by 8
Mancilla, S., Wences, G., Hernández–López, E., & Cohen, I. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01112-x · Cited by 9
“As a feature selection method, TDA can help reduce dimensionality, improve the interpretation of results, and help analyze complex databases; see [6, 28, 39].”
Jeung, S.-Y., & Kwon, J.-W. (2025) · Sensors · doi:10.3390/s25092709 · Cited by 8
“It is expected to contribute to delivering reliable predictions in multi-sensor predictive maintenance systems [8–10].”
Topological data analysis approach to time series and shape analysis of dynamical system
Shah, W. H., Fatima, S. R., Huerta-Cuellar, G., et al. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0268340 · Cited by 8
Informed machine learning for complex data
Oneto, L., Navarin, N., Micheli, A., Pasa, L., Gallicchio, C., Bacciu, D., & Anguita, D. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132505 · Cited by 16
Ellis, S. P., & Klein, A. (2014) · Homology Homotopy and Applications · doi:10.4310/hha.2014.v16.n1.a14 · Cited by 8
“In particular, persistent homology has been applied to brain data [Lee et al., 2011, Chung et al., 2009].”
MethodTopological Data Analysis with Bregman Divergences
Edelsbrunner, H., & Wagner, H. (2016) · Journal of computational geometry · doi:10.20382/jocg.v9i2a6 · Cited by 12
Resting state dynamic functional connectivity: Network topology analysis
Knyazeva, I., Poyda, A. A., Orlov, V. A., Verkhlyutov, V. M., Makarenko, N., Kozlov, S., Velichkovsky, B. M., & Ushakov, V. (2017) · Biologically Inspired Cognitive Architectures · doi:10.1016/j.bica.2017.10.001 · Cited by 9
A Visual Analytics Framework for Analysis of Patient Trajectories
Madhobi, K. F., Kamruzzaman, M., Kalyanaraman, A., Lofgren, E., Moehring, R. W., & Krishnamoorthy, B. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3342143 · Cited by 7
“Algebraic topology is the branch of mathematics dealing with the shape and connectivity of spaces [1, 14].”
BackgroundBizhani, M., & Ardakani, O. H. (2021) · Energy & Fuels · doi:10.1021/acs.energyfuels.1c03255 · Cited by 8
A Study of Monodromy in the Computation of Multidimensional Persistence
Cerri, A., Ethier, M., & Frosini, P. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37067-0_17 · Cited by 8
“[6]).”
Vietoris thickenings and complexes have isomorphic homotopy groups
Adams, H., Frick, F., & Virk, Ž. (2022) · Journal of Applied and Computational Topology · doi:10.1007/s41468-022-00106-5 · Cited by 8
Confinement in non-Abelian lattice gauge theory via persistent homology
Spitz, D., Urban, J. M., & Pawlowski, J. M. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.107.034506 · Cited by 16
Zigzag persistence for coral reef resilience using a stochastic spatial model
McDonald, R. A., Neuhausler, R., Robinson, M. J., Larsen, L. G., Harrington, H. A., & Bruna, M. (2023) · Journal of The Royal Society Interface · doi:10.1098/rsif.2023.0280 · Cited by 10
“TDA is a branch of computational mathematics that summarises the shape of data through topological invariants [11, 21].”
Background
Metadata
- Authors
- Carlsson, G.
- Affiliations
- Stanford University (US)
- Corresponding author
- Carlsson, Gunnar
- ORCID
- Carlsson, Gunnar
- Venue
- Bulletin of the American Mathematical Society
- Publisher
- American Mathematical Society (AMS)
- Published
- 2009-01-29
- Volume
- 46
- Issue
- 2
- Pages
- 255-308
- ISSN
- 0002-9904 (ISSN-L)
- 1088-9485
- 0273-0979
- OpenAlex
- W1991566301
- Cited by
- 2,302
- References
- 78
- Citations by year
- 2026: 215 · 2025: 216 · 2024: 158 · 2023: 176 · 2022: 159 · 2021: 215 · 2020: 227 · 2019: 151 · 2018: 163 · 2017: 132 · 2016: 128 · 2015: 95 · 2014: 99 · 2013: 65 · 2012: 46
- FWCI
- 33.43
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Bulletin of the American Mathematical Society (publishedVersion)
- Funders
- National Science Foundation (0354543)
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Mathematics › Geometry and Topology › Geometric and Algebraic Topology
- Physical Sciences › Mathematics › Applied Mathematics › Geometric Analysis and Curvature Flows
- Keywords
- point clouds, distance measures, high-dimensional data, biological data, microarray data, high-throughput data
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