The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks
Open accessAbstract
Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
The Evolution of Random Graphs
(1986) · CBMS Regional Conference Series in Mathematics · doi:10.1090/cbms/062/06 · Cited by 5,543
Szklarczyk, D., Franceschini, A., Kuhn, M., Simonovic, M., Roth, A., Minguez, P., Doerks, T., Stark, M., Muller, J., Bork, P., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq973 · Cited by 3,460
The Human Connectome: A Structural Description of the Human Brain
Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260
Controllability of complex networks
Liu, Y.-Y., Slotine, J.-J., & Barabási, A.-L. (2011) · Nature · doi:10.1038/nature10011 · Cited by 3,215
The link‐prediction problem for social networks
Liben‐Nowell, D., & Kleinberg, J. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20591 · Cited by 3,043
Friends and neighbors on the Web
Adamic, L. A., & Adar, E. (2003) · Social Networks · doi:10.1016/s0378-8733(03)00009-1 · Cited by 2,954
Link prediction in complex networks: A survey
Lü, L., & Zhou, T. (2011) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2010.11.027 · Cited by 2,819
Cited by
Drug target prioritization by perturbed gene expression and network information
Isik, Z., Baldow, C., Cannistraci, C. V., & Schroeder, M. (2015) · Scientific Reports · doi:10.1038/srep17417 · Cited by 135
Modular gateway-ness connectivity and structural core organization in maritime network science
Xu, M., Pan, Q., Muscoloni, A., Xia, H., & Cannistraci, C. V. (2020) · Nature Communications · doi:10.1038/s41467-020-16619-5 · Cited by 103
Self-citationFarooqui, A., Tazyeen, S., Ahmed, M. M., Alam, A., Ali, S., Malik, M. Z., Ali, S., & Ishrat, R. (2018) · Scientific Reports · doi:10.1038/s41598-018-28375-0 · Cited by 30
Metadata
- Authors
- Cannistraci, C. V., Alanis-Lobato, G., & Ravasi, T.
- Venue
- Scientific Reports
- Published
- 2013-04-08
- Volume
- 3
- Issue
- 1
- Pages
- 1613-1613
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2066692087
- Cited by
- 401
- References
- 39
- FWCI
- 13.39
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. https://app.cochat.ai/papers/10.1038/srep01613
- The paper
- Cannistraci, C. V., Alanis-Lobato, G., & Ravasi, T. (2013). From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. Scientific Reports. https://doi.org/10.1038/srep01613
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.