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.
plainstates a cause- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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- 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)
hedgedstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- 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.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- 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)
plainstates a cause- Asserted: passes — The authors put it forward as true.
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- 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)
plainstates an association- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- 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.
plainstates an association- 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.
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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)
plainstates a cause- 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.
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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)
plainstates an association- 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.
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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
boostedstates an association- 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.
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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.
hedgedstates an associationSingle 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
hedgedstates an associationSingle 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
hedgedstates a cause- 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
plainstates a cause- 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
boostedstates an association- 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.
hedgedstates an association- 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
hedgedstates an association- 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.
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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
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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
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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
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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
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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
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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
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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
boostedstates a cause- Asserted: passes — The authors put it forward as true.
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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.
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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.
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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.
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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.
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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
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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.
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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
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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
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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
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
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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.
plainstates a cause- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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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
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
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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
hedgedstates an association- Asserted: passes — The authors put it forward as true.
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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.
plainstates an association- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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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.
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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.
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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.
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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.
plainstates an association- Asserted: passes — The authors put it forward as true.
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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.
boostedstates a cause- Asserted: passes — The authors put it forward as true.
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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.
plainstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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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.
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
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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
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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
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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
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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.
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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.
plainstates an association- Asserted: passes — The authors put it forward as true.
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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
plainstates an association- Asserted: passes — The authors put it forward as true.
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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.
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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.
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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.
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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.
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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.
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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.
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
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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
plainstates a cause- 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.
plainstates a cause- 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
plainstates an association- 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
plainstates an association- 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
boostedstates a cause- 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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“We note that the correspondence X → π0(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 π0(f) : π0(X)→ π0(Y ), satisfying certain obvious conditions on composite maps and identity maps.”
BackgroundMethodDensity Estimation for Statistics and Data Analysis
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Cited by
Sorted by citations
Items 2,101–2,200 of 2,331
Conditional Graph Diffusion with Topological Constraints for Brain Network Generation
Park, J., Lee, D., Guorong Wu, A., & Kim, W. H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05162-2_22
Topological Data Analysis for Sustainable and Smart City Education Systems
de Zarzà, I., & de Curtò, J. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-5069-9_50
On the Size of Chromatic Delaunay Mosaics
Biswas, R., di Montesano, S. C., Draganov, O., et al. (2025) · Discrete & Computational Geometry · doi:10.1007/s00454-025-00778-7
“Methods from the field of topological data analysis [3], which are particularly suited for point cloud data, suggest themselves as a natural candidate to extract geometric features from such datasets.”
Tran‐Dinh, A., Atchade, E., Tanaka, S., Lortat‐Jacob, B., Castier, Y., Mal, H., Messika, J., Mordant, P., Montravers, P., & Morilla, I. (2025) · medRxiv · doi:10.1101/2025.10.01.25337124
Alvarado, E., Gupta, P., & Krishnamoorthy, B. (2025) · Journal of Applied and Computational Topology · doi:10.1007/s41468-025-00220-0
“The resulting VR persistence diagram (PD) of X summarizes its topology across multiple scales, and could have implications for the application generating the data [5, 13].”
Ramos, R. H., Carels, N., Scardini, R., Franca, L. L., Simao, A. D. S., Ferreira, C. D. O. L., & Carneiro, F. R. G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.08.681235
“PH is a method of topological data analysis [23].”
Nested Ensemble Learning with Topological Data Analysis for Graph Classification and Regression
Abaa, I., & Islambekov, U. (2025) · International Journal of Topology · doi:10.3390/ijt2040017
“Rooted in algebraic topology and computational geometry, TDA offers tools to study the shape structure underlying data [2].”
Using Persistent Landscapes in Image Processing
Abakumov, A. V., & Eremeev, S. (2025) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661825700270
“This article discusses methods of image processing based on persistent homology [9], which is a mathematical basis for constructing topological characteristics in the form of a barcode from an image, which allows solving problems of decomposition, segmentation, classification, filtering, etc.”
Topological decoding of grid cell activity via path lifting to covering spacesPreprint
Yao, Y., & Yoon, I. H. R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.17.683158
“For a general introduction to persistent cohomology, we refer the reader to SI Section 1.2 and [7, 13, 14, 21].”
InfluentialElementary Methods for Persistent Homotopy Groups
Adams, H., Batan, M. A., Pamuk, M., & Varlı, H. (2025) · Discrete & Computational Geometry · doi:10.1007/s00454-025-00781-y
“This theory has many applications and has become a central tool in topological data analysis [11, 19].”
Cycling signatures: Identifying cycling motions in time series using algebraic topology
Bauer, U., Hien, D., Junge, O., et al. (2025) · Journal of Computational Dynamics · doi:10.3934/jcd.2025007
“The advantage of this approach is that algebraic topology provides well established, robust, dimension independent tools to identify and quantify the required ‘holes’ in the time series data [11, 9, 5, 23].”
Akhalwaya, I. Y., Bhayat, A., Connolly, A., et al. (2025) · Quantum · doi:10.22331/q-2025-10-31-1901
“The Betti numbers have a long history in computational algebraic topology and data analysis: For fixed k , polynomial-time algorithms for Betti number estimation date back to the 1970s [1], and many recent applications of this problem have been found in the field of Topological Data Analysis [2–4].”
Persistent homology of music network with three different distances
Heo, E., Choi, B., Kim, M. O., et al. (2025) · Journal of Mathematics and Music · doi:10.1080/17459737.2025.2568844
“…relatively recently compared to other traditional data analysis tools, has proven its usefulness in various applications including music analysis (Bergomi and Baratè 2020; Bigo and Andreatta 2019; Carlsson 2009; Cohen-Steiner, Harer, and Edelsbrunner 2007; Edelsbrunner and Harer 2009; Zomorodian…”
Flow-aware ellipsoidal filtration for persistent homology of recurrent signals
Eryilmaz, O. B., Katar, C., & Little, M. A. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0317749
Maximum Betti Numbers of Čech Complexes
Edelsbrunner, H., & Pach, J. (2025) · Discrete & Computational Geometry · doi:10.1007/s00454-025-00796-5
“Of particular interest are the Betti numbers , which may be interpreted as the numbers of holes of different dimensions.”
Abdullahi, M. S., Piro, R. M., Suratanee, A., & Plaimas, K. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.11.018
“Advances in computational topology, including persistent homology (PH), have introduced tools for analyzing multi-scale topological features [17,18], such as connected components and loops, across different filtration values (i.e., threshold parameters for an appropriate distance measure, that…”
Regime-Adaptive Identification of Dengue Transmission Hubs Using Discrete Morse Theory in BrazilPreprint
Trunci, E. C., Trunci, J. G. C., Dias, J. F., & de Barros, L. G. (2025) · medRxiv · doi:10.1101/2025.11.09.25339847
“Topological data analysis (TDA) extracts structural information from high-dimensional data (11; 12; 25).”
Duch, W. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228448
“Carlsson [50] argues that geometry and topology can be used to analyze various kinds of data.”
Mishra, A., & Motta, F. C. (2025) · Journal of Applied and Computational Topology · doi:10.1007/s41468-025-00225-9
“Often, and for all data considered in this study, a dataset is a finite point cloud in some metric space.”
TopoRN: A topology-aware reconstruction network for infrared image super-resolution
Dan, J., Jin, T., Chi, H., Luo, Z., Cao, K., Xinjing, Y., & Yang, X. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06999-w · Cited by 1
“To effectively inject the topological features 3 Background: Topological data analysis Topological data analysis (TDA) [58 ] is a rapidly developing field that aims to extract the inherent topological fea - tures of data.”
Muñoz, D., Navarro, J. M., Allix, O., et al. (2025) · Lecture notes in computational science and engineering · doi:10.1007/978-3-031-95709-3_5
Zabaleta-Ortega, Á., Masoller, C., & Guzmán-Vargas, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-27083-w · Cited by 1
Domínguez-Monterroza, A., Mateos, A., & Martín, A. J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0337620 · Cited by 1
“This method characterizes the shape of data by identifying persistent topological features that are robust to noise and provide a signature of the underlying dynamics [40].”
Topology-Based Optimization of Image Symmetry Axes Using Wasserstein Distance
Liu, T., Li, T., Li, L., & He, Y. (2025) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei68103.2025.11259429
“The evolution of these topological features is formalized through persistent homology [12].”
Clustering Problem of Stock Price Time Series Based on Topological Data Analysis
Li, X., & Zhang, H. (2025) · International Conference on Economic Management and Big Data Application (ICEMBDA) · doi:10.1145/3770177.3770248
“Topological Data Analysis was originally formalized by Edels-brunner [3], Zomorodian [4], and further developed by Carlsson [7], who laid its theoretical foundations and demonstrated its potential for uncovering hidden structures in complex data.”
InfluentialTHE COMBINATORICS OF WEIGHTED COHOMOLOGY
Bura, A. C., He, Q., & Reidys, C. M. (2025) · Rocky Mountain Journal of Mathematics · doi:10.1216/rmj.2025.55.1563
Cellular deconvolution of the brain with topological magnetic resonance image analysisPreprint
Vazquez, L. A., Fromandi, M. B., Hagemann, T. L., Risgaard, R. D., Guerrero-Gonzalez, J. M., Singh, A. P., Frautschi, P. C., Hurley, S. A., Sousa, A. M. M., Dean, D. C., III, et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.10.693426
“Recent mathematical developments have led to the emergence of topological data analysis (TDA), the application of topological methods to analyze complex and large datasets (40) .”
Diagnostics of Semantic Word Spaces
Isheden, G. (2013) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
Additional file 1 of HiDeF: identifying persistent structures in multiscale ‘omics data
Zheng, F., Zhang, S., Churas, C., Pratt, D., Bahar, I., & Ideker, T. E. (2021) · Figshare · doi:10.6084/m9.figshare.13544254
Additional file 2 of HiDeF: identifying persistent structures in multiscale ‘omics data
Zheng, F., Zhang, S., Churas, C., Pratt, D., Bahar, I., & Ideker, T. E. (2021) · Figshare · doi:10.6084/m9.figshare.13544257
Carr, E., Carrière, M., Michel, B., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16650497
On the support of betti tables of multiparameter persistent homology modules
Guidolin, A., & Landi, C. (2025) · The Quarterly Journal of Mathematics · doi:10.1093/qmath/haaf021
“One of the main concepts in Topological Data Analysis is persistent homology , a tool to capture topological information at multiple scales and provide meaningful topological summaries of the data, as surveyed, for example, in [Ghr07, Car09, EM12].”
Unsupervised learning of collective patterns in self-propelled particles through persistent homology
Suetani, H. (2025) · EPJ Web of Conferences · doi:10.1051/epjconf/202533402002
Topological Data Analysis of China’s Stock Market Risks to Detect Early Warning SignalsPreprint
Cong, Y., Zhang, W., Shen, R., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5501819
FUNDAMENTOS ESSENCIAIS PARA CIÊNCIA DE DADOS
Mendes, M. T. C. F. (2025) · Edifes eBooks · doi:10.36524/9786553310230
Discrete level set persistence for finite discrete functions
Belton, R., & Essl, G. (2025) · Foundations of Data Science · doi:10.3934/fods.2025022
Toward structure-preserving quantum encodings
Parzygnat, A. J., Bradley, T.-D., Vlasic, A., & Pham, A. (2025) · Physical Review Research · doi:10.1103/rph8-g15q · Cited by 2
On intrinsic dimension of point clouds by a persistent homology approach: computational tips
Bandiziol, C., Marchi, S. D., & Allegra, M. (2025) · Modern Mathematical Methods · doi:10.64700/mmm.89
“Followingthe recent surge of topological data analysis [15, 16, 17], several authors have explored topological approaches to ID estimation [1, 63], that do not fall within the two classes defined above, as they are insensitive to the particular metric chosen to define distances in the data set.”
AutoEncoding Cognitive Maps of Non-Trivial Topology
Beketov, M., Sorokin, K., Subbotin, M., Sotskov, V. P., Анохин, К. В., & Ayzenberg, A. A. (2026) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-032-01279-1_9
Predicting protein–nucleic acid flexibility using persistent sheaf Laplacians
Hayes, N., Merkurjev, E., & Wei, G.-W. (2026) · Physical Chemistry Chemical Physics · doi:10.1039/d5cp04098a
“38,39 TDA has been widely applied to molecular sciences.”
IODC-3D: A Low-Cost and Scalable Framework for Generating 3D Digital Cousins of Irregular ObjectsPreprint
Khan, Z., Pan, Y., Wang, D., Li, H., & Chen, A. R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6017018
From Darwin to teleonomy: A categorical final-cause calculus for evolution
Patrascu, A. T. (2026) · Biosystems · doi:10.1016/j.biosystems.2025.105687
Topological Deep Learning for Coral Reefs
Aguilar-Ortíz, J., Díaz-Parra, O., Ruíz-Vanoye, J. A., et al. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-5881-9.ch008
Persistent homology spatial pyramid bag-of-words for topological feature learning
Yi, L., & Peng, N. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114630 · Cited by 2
Persistence Module and Schubert Calculus
Hiraoka, Y., Yahiro, K., & Xu, C. (2026) · Springer eBooks · doi:10.1007/978-981-95-1446-5_12
Asriani, E., Muchtadi-Alamsyah, I., & Purwarianti, A. (2025) · International Conference on Advanced Informatics: Concept, Theory and Application (ICAICTA) · doi:10.1109/icaicta67604.2025.11335036
“TDA applies tools from algebraic topology, particularly persistent homology, to capture the intrinsic shape of data, revealing hidden structures such as loops, holes, and higher-dimensional connections that conventional vector-based approaches may overlook [11], [15].”
Truth as geometry: a topological approach to logic, uncertainty, and AI reasoning
Pourmoslemi, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-343852-3.00020-6
Tudoreanu, M. E., & Sakthivel, M. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1013726
Unitary similarity comparison of persistent homology groups
He, J., Hou, B., Wu, T., & Cao, Y. (2026) · Japan Journal of Industrial and Applied Mathematics · doi:10.1007/s13160-025-00753-y
“Topological Data Analysis (TDA) is a ubiquitous tool for classification (See the sur - veys [1, 2 ]).”
Quantum gravity and effective topology
van der Duin, J., Loll, R., Schiffer, M., & Silva, A. (2026) · The European Physical Journal C · doi:10.1140/epjc/s10052-026-15322-x
“The geometric variable characterizing a spatial slice at integer t is its one-dimensional volume, which in lattice units is given by the number (cid:13)( t ) of its edges, where we demand (cid:13)( t ) ≥ 3 to ensure that T is a simplicial manifold.”
Paul, A., Rana, A., Chattopadhaya, I., et al. (2025) · International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) · doi:10.1109/icrcicn68210.2025.11364933
“Topological data analysis (TDA) provides mathematical tools for analyzing the shape and structure of high-dimensional data [1].”
Algorithms and Complexity Results for K-Theoretic Persistent Homology
Maruyama, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-17801-5_5
Vayyala, R. (2025) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon67450.2025.11381053
“TDA has successfully been applied in a vast array of fields ranging from neuroscience to material science [1].”
Emergent Latent Structure via Co-Reactivity to Shared ConstraintsPreprint
ursachi, A. (2026) · Research Square · doi:10.21203/rs.3.rs-8823860/v1
Sanepalli, U. R. (2022) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit22557 · Cited by 6
Classification of epilepsy MEG data using topological data analysis and artificial neural network
Han, Y., Chung, C. K., & Kim, J. H. (2025) · Korean Journal of Applied Statistics · doi:10.5351/kjas.2025.38.2.189
Topological Data Analysis of Departure Delay Co-occurrence in Japanese Domestic Aviation System
Sadeek, S. N., Hanaoka, S., & Sugishita, K. (2026) · Data Science for Transportation · doi:10.1007/s42421-026-00150-y
“To reveal these hidden dynamics in terms of departure delay of flight operation, we used Vietoris–Rips (VR) simplicial complexes (Carlsson 2009)—one of the most popular TDA filtration methods which track the emergence of disappearance of topological features across a sequence of delay thresholds…”
Vayyala, R. (2025) · Proceedings · doi:10.1109/icdm65498.2025.00167
“TDA has successfully been applied in a vast array of fields ranging from neuroscience to material science [1].”
Zdražil, J., Kong, L., Spíchal, L., et al. (2026) · Plant Phenomics · doi:10.1016/j.plaphe.2026.100186
Maximum persistent Betti numbers of Čech complexes
Edelsbrunner, H., Kahle, M., & Kanazawa, S. (2026) · Journal of Applied and Computational Topology · doi:10.1007/s41468-026-00233-3
“Hence, the boundary of the convex hexagon that passes through the six points is a non-trivial 1-cycle in ˇ C 1 + ε , and for ε ≤ √ 2 − 1, it already exists in ˇ C 1 .”
In the Beginning Was ... Gaussian Topology?!
Adler, R. J. (2026) · The Mathematical Intelligencer · doi:10.1007/s00283-026-10503-8
Topological Data Analysis for Unsupervised Feature Selection in Large Scale Spatial Omics Data Sets
Boyle, J. F., Hamm, G. R., Williams, E. C., Hartman, R. J. G., Soderburg, M., Henry, I., & Casey, M. (2026) · Bulletin of Mathematical Biology · doi:10.1007/s11538-026-01618-2
“We present an approach based on persistent homology (PH) [7, 8] that quantifies spatial variability via the topological activity in a gene’s expression pattern [9].”
Mapping Higher-Order Topology in OCD Brain Networks with Hodge LaplacianPreprint
Ruan, H., Chung, M. K., Bruin, W. B., Džinalija, N., Abe, Y., Alonso, P., Anticevic, A., Balachander, S., Batistuzzo, M. C., Benedetti, F., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.04.709586 · Cited by 1
“& In classical PH, a sequence of nested simplicial complexes, which usually refers to a filtration, is constructed (63).”
Topological Robust Reinforcement Learning
Goel, J., Jacob, R. A., Zhang, J., & Gel, Y. R. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401746
“…for RL. a) Background on Topological Data Analysis: Intu-itively, the idea of TDA and its subfield of persistent homology (PH) is to look at the data X at varying resolutions and track various patterns in X as they appear and disappear when we monotonically change the resolution thresholds [6, 7].”
Uncovering Treatment Response Patterns with Topological Clustering of Brain Networks
Yi, J., Yin, J., Ghosal, R., et al. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401042
“The fact that the overall topological changes hold more significance over fleeting structures in PH makes the algorithm particularly robust under the presence of noise and artifacts, thus revealing more insight on network topology than single-scale measures [4].”
Zheng, Y., Lv, J., Wu, Y., et al. (2026) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2026.3670538
“A Persistence Diagram (PD) is a topological tool that characterizes the evolution of features—such as connected components and loops—over a range of filtration scales [41], [42].”
Garví-Gualda, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6390762
Quantifying Function Simplicity by Arc Length: A Geometric Foundation for Explainable AI
Tozawa, A., Murayama, K., Oda, R., et al. (2025) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai-winter69777.2025.00062
“Topological data analysis (TDA) provides tools for understanding the shape of data and functions through persistent homology [10], [11].”
Topological model selection: a case-study in tumour-induced angiogenesis
McDonald, R. A., Byrne, H. M., Harrington, H. A., Thorne, T., & Stolz, B. J. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag065
“TDA offers a toolkit of methods for quantifying spatial data (Ghrist 2008, Carlsson 2009, Edelsbrunner and Harer 2010).”
Glavchev, M., Hlavcheva, Y., Lipchanskyi, M., et al. (2026) · Системи управління навігації та зв’язку Збірник наукових праць · doi:10.26906/sunz.2026.1.051
Ayariga, S. A. (2026) · Research Square · doi:10.21203/rs.3.rs-9125450/v1
Li, Z., Wu, X., Ling, X., & Tu, C. (2026) · Advances in Physics X · doi:10.1080/23746149.2026.2645192
“Through this synthesis, we aim to provide a roadmap for the rigorous compression of complex systems, enabling the discovery of the effective laws that govern high-dimensional chaos 6,14,15 .”
Jiang, X., Romli, N. A., Gao, Y., et al. (2026) · AIP conference proceedings · doi:10.1063/5.0316212
Afifi, B., Abu-Elkheir, M., & Abdennadher, S. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.01.036
Dey, A. K., & Gel, Y. R. (2026) · Technometrics · doi:10.1080/00401706.2026.2643214
“Persistent homology (PH) is an arsenal of tools from computational topology and is a subfield of topological data analysis that allows us to quantify the shape of an object along various geometric dimension(s) (Zomorodian and Carlsson 2005; Edelsbrunner and Harer 2008; Carlsson 2009).”
Topological Metric for Unsupervised Embedding Quality Evaluation
Shestov, A., Klenitskiy, A., Denisova, D., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21300-6_51
Persistent Local Laplacian Prediction of Protein–Ligand Binding Affinities
Liu, J., & Feng, H. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c02460 · Cited by 1
“…36 is a widely recognized, curated resource that systematically collects experimentally determined 3D structures of protein − ligand complexes alongside their corresponding binding affinity data, e.g., dissociation constants K d , inhibition constants K i , and Gibbs free energy changes Δ G .”
A survey of simplicial, relative, and chain complex homology theories for hypergraphs
Gasparovic, E., Purvine, E., Sazdanović, R., Wang, B., Wang, Y., & Ziegelmeier, L. (2026) · Journal of Applied and Computational Topology · doi:10.1007/s41468-025-00227-7 · Cited by 3
“While embedded homology is well-studied and applied, there remain open questions that will help build intuition and characterize the relationship between structure in H and structure in H emb”
Time series analysis of spiking neural systems via transfer entropy and directed persistent homology
Peek, D., Pritam, S., Skerritt, M. P., & Chalup, S. K. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133541 · Cited by 1
Structures for deep learning and topology optimization of functions on 3D shapes
Poulenard, A. (2020) · theses.fr (ABES) · doi:10.70675/6044d6a1zfadbz445fzbedezf0da1fdd6ec8
Mesure d'interactions locales pour les nœuds d'un réseau complexe : approches théorique et pratique
Djellabi, M. (2021) · theses.fr (ABES) · doi:10.70675/979a9261z0d75z49b7z91ffz00d9be923e19
Beaufils, B. (2020) · theses.fr (ABES) · doi:10.70675/803ee8dbzbb85z4d09za979z798c923c9cb9
Resilience, Tipping Points, and Hysteresis
Grindrod, P. (2026) · Complexities · doi:10.3390/complexities2020010
“One of the earliest systematic treatments of topology for dynamical data was given in 2009 by [14], which presented TDA as a general framework for analysing complex datasets, including those arising from dynamical processes.”
Detecting unique wind field features in hurricane Sandy from topological data maps
Hoffmeier, J. (2026) · Environmental Data Science · doi:10.1017/eds.2026.10035
Forest, F. (2021) · theses.fr (ABES) · doi:10.70675/5187c565z8bbaz452bzaa6ez7a367a0b16f0 · Cited by 1
Modelling the geometry of high-dimensional omics data : dimensionality, subspaces, trajectories
Bac, J. (2022) · theses.fr (ABES) · doi:10.70675/3c682a3dz1b61z4109z9dbdz18ac2365aed0
Calcul incrémental des groupes d'homologie d'un objet au cours d'un processus de construction
Rharbaoui, W. (2022) · theses.fr (ABES) · doi:10.70675/9062de34z9c39z4cecz9a0cz1cef4dc341ca
Development of trustworthy intelligent avatars in virtual immersion
Hadadi, A. (2024) · theses.fr (ABES) · doi:10.70675/8d13bedfz1589z4486zbc0bzdf696c7a3007
Analyse biogéométrique de l'évolution des protéines et des traits d'histoire de vie
Dagher, L. B. (2024) · theses.fr (ABES) · doi:10.70675/3a39f4f2zb57dz4fb3z8990zbd5d3fe1a6bf
Wu, Y., Kang, Y., Gu, J., Zhu, H., & Li, Z. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0080
“Therefore, combining topological data analysis (TDA) [ 29 ] with existing graph-based data analysis can help better capture the regulatory relationships between genes.”
Hu, C., Czajkowsky, D. M., Liang, J., & Shao, Z. (2026) · Biophysical Journal · doi:10.1016/j.bpj.2026.04.010
Choi, Y. J., Bharath, K., & Kurtek, S. (2026) · ICSA book series in statistics · doi:10.1007/978-3-032-06649-7_13
Ferracina, F., Lu, A. K. A., Du, X. N., et al. (2026) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ae6216
“To examine this behavior, we combine conventional structural analysis with persistent homology (PH) (Carlsson 2009, Edelsbrunner and Harer 2010), which captures multi-scale topological features (loops and voids that persist across different length scales), providing insights into medium-range order…”
Topseg: A Multi-Scale Topological Framework for Data-Efficient Heart Sound Segmentation
Zhangm, P., Li, Z., Liu, Y., et al. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462800
Lin, Z., Zulkepli, N. F. S., Kasihmuddin, M. S. M., & Gobithaasan, R. U. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115963
Adaptive Topological Constraint Enhanced Pedestrian Trajectory Prediction
Zhao, Z., Lai, J., Chen, S., et al. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461902
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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