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Finding and evaluating community structure in networks
Open accessAbstract
We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
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“After the pioneering PhenoGraph method (Levine et al, 2015), the standard approach to clustering single‐cell datasets has become multi‐resolution modularity optimization (Newman & Girvan, 2004; Reichardt & Bornholdt, 2006) as implemented in the Louvain algorithm (Blondel et al, 2008) on single‐cell KNN graphs.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Newman & Girvan, 2004
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Metadata
- Authors
- Newman, M. E. J., & Girvan, M.
- Affiliations
- Santa Fe Institute (US)
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2004-02-26
- Volume
- 69
- Issue
- 2
- Pages
- 026113-026113
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W2095293504
- Cited by
- 13,931
- References
- 59
- Citations by year
- 2026: 370 · 2025: 648 · 2024: 663 · 2023: 685 · 2022: 816 · 2021: 872 · 2020: 880 · 2019: 963 · 2018: 988 · 2017: 803 · 2016: 888 · 2015: 781 · 2014: 844 · 2013: 787 · 2012: 655
- FWCI
- 105.23
- 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 clustering, edge deletion, betweenness centrality, number of communities, network partitioning, complex networks
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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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