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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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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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