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Robustness of community structure in networks
Open accessAbstract
The discovery of community structure is a common challenge in the analysis of network data. Many methods have been proposed for finding community structure, but few have been proposed for determining whether the structure found is statistically significant or whether, conversely, it could have arisen purely as a result of chance. In this paper we show that the significance of community structure can be effectively quantified by measuring its robustness to small perturbations in network structure. We propose a suitable method for perturbing networks and a measure of the resulting change in community structure and use them to assess the significance of community structure in a variety of networks, both real and computer generated.
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“Regarding the robustness of the solution as quantified using VI, the curves for the empirical and random data remain distinct and thus the network of individuals can be said to show community structure (see Figure 6b; [24]).”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [24]
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Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
Self-citationFinding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931
Self-citationElements of Information Theory
Cover, T. M., & Thomas, J. A. (2005) · doi:10.1002/047174882x · Cited by 13,791
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
Self-citationFinding community structure in very large networks
Clauset, A., Newman, M. E. J., & Moore, C. (2004) · Physical Review E · doi:10.1103/physreve.70.066111 · Cited by 7,390
Self-citationObjective Criteria for the Evaluation of Clustering Methods
Rand, W. M. (1971) · Journal of the American Statistical Association · doi:10.1080/01621459.1971.10482356 · Cited by 6,493
Fast algorithm for detecting community structure in networks
Newman, M. E. J. (2004) · Physical Review E · doi:10.1103/physreve.69.066133 · Cited by 5,415
Self-citationThe Large-Scale Organization of Metabolic Networks
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35036627 · Cited by 4,972
An Information Flow Model for Conflict and Fission in Small Groups
Zachary, W. W. (1977) · Journal of Anthropological Research · doi:10.1086/jar.33.4.3629752 · Cited by 4,709
Flagged first, then by citations
Research works printed but not linked: 5
- 10.1073/pnas.0601602103 · DOI · Paper page
- T. Łuczak, 1992, Proceedings of the Symposium on Random Graphs, Poznań, 1989
- 10.2307/2284239 · DOI
- M. Meila, 2005, ICML ’05: Proceedings of the 22nd International Conference on Machine Learning
- L. Danon, J. Stat. Mech.: Theory Exp., 2005, P09008, 10.1088/1742-5468/2005/09/P09008 · DOI
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Good, B. H., de Montjoye, Y.-A., & Clauset, A. (2010) · Physical Review E · doi:10.1103/physreve.81.046106 · Cited by 937
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Weight-conserving characterization of complex functional brain networks
Rubinov, M., & Sporns, O. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.069 · Cited by 903
Statistical properties of community structure in large social and information networks
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Fair, D. A., Bathula, D., Nikolas, M. A., & Nigg, J. T. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1115365109 · Cited by 464
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Fair, D. A., Nigg, J. T., Iyer, S., Bathula, D., Mills, K. L., Dosenbach, N. U. F., Schlaggar, B. L., Mennes, M., Gutman, D., Bangaru, S., et al. (2013) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00080 · Cited by 463
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Karalunas, S. L., Fair, D., Musser, E. D., Aykes, K., Iyer, S. P., & Nigg, J. T. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.763 · Cited by 340
RetractedKovács, I. A., Palotai, R., Szalay, M., & Csermely, P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012528 · Cited by 344
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Tasgin, M., Herdağdelen, A., & Bingol, H. O. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0711.0491 · Cited by 277
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Ronhovde, P., & Nussinov, Z. (2009) · Physical Review E · doi:10.1103/physreve.80.016109 · Cited by 244
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Fond, T. L., & Neville, J. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772752 · Cited by 221
Stevens, A. A., Tappon, S. C., Garg, A., & Fair, D. A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030468 · Cited by 212
Robust network community detection using balanced propagation
Šubelj, L., & Bajec, M. (2011) · The European Physical Journal B · doi:10.1140/epjb/e2011-10979-2 · Cited by 225
Functional Connectome of the Fetal Brain
Turk, E., van den Heuvel, M. I., Benders, M. J., de Heus, R., Franx, A., Manning, J. H., Hect, J. L., Hernandez-Andrade, E., Hassan, S. S., Romero, R., et al. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.2891-18.2019 · Cited by 176
Finding communities in directed networks
Kim, Y., Son, S., & Jeong, H. (2010) · Physical Review E · doi:10.1103/physreve.81.016103 · Cited by 173
Community extraction for social networks
Zhao, Y., Levina, E., & Zhu, J. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1006642108 · Cited by 162
Self-citationFunctional connectivity hubs of the mouse brain
Liska, A., Galbusera, A., Schwarz, A. J., & Gozzi, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.033 · Cited by 171
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Yamamoto, H., Moriya, S., Ide, K., Hayakawa, T., Akima, H., Sato, S., Kubota, S., Tanii, T., Niwano, M., Teller, S., et al. (2018) · Science Advances · doi:10.1126/sciadv.aau4914 · Cited by 128
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Ashourvan, A., Telesford, Q. K., Verstynen, T., Vettel, J. M., & Bassett, D. S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215520 · Cited by 65
Measuring robustness of community structure in complex networks
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Wang, Y., Di, Z., & Fan, Y. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027418 · Cited by 59
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Delmotte, A., Tate, E. W., Yaliraki, S. N., & Barahona, M. (2011) · Physical Biology · doi:10.1088/1478-3975/8/5/055010 · Cited by 57
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Teller, S., Tahirbegi, I. B., Mir, M., Samitier, J., & Soriano, J. (2015) · Scientific Reports · doi:10.1038/srep17261 · Cited by 54
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Defining and Discovering Communities in Social Networks
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Carbonell, F., Nagano‐Saito, A., Leyton, M., Cisek, P., Benkelfat, C., Yong He, A., & Dagher, A. (2014) · Neuropharmacology · doi:10.1016/j.neuropharm.2013.12.021 · Cited by 47
Park, Y., & Bader, J. S. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts211 · Cited by 45
Link-Prediction Enhanced Consensus Clustering for Complex Networks
Burgess, M., Adar, E., & Cafarella, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153384 · Cited by 46
Stable Community Cores in Complex Networks
Seifi, M., Junier, I., Rouquier, J., Iskrov, S., & Guillaume, J. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-30287-9_10 · Cited by 43
Metadata
- Authors
- Karrer, B., Levina, E., & Newman, M. E. J.
- Affiliations
- University of Michigan (US)
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2008-04-29
- Volume
- 77
- Issue
- 4
- Pages
- 046119-046119
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W2136088806
- Cited by
- 350
- References
- 47
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 5 · 2023: 4 · 2022: 9 · 2021: 21 · 2020: 18 · 2019: 23 · 2018: 15 · 2017: 12 · 2016: 19 · 2015: 21 · 2014: 34 · 2013: 31 · 2012: 32
- FWCI
- 21.90
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review E (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- community structure, network robustness, perturbation analysis, network perturbation, community detection
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 ›
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