The DOI matches its Crossref record; 57 of the 59 references it cites were found in a registry or an index.
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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Cites
(1979) · Cited by 43,841
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Statistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Self-citationAdhi Harmoko, S., M.Komp, Jacquard, J. M., Zuse, K., & Eniac (2005) · Cited by 18,035
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-citationA set of measures of centrality based upon betweenness
Freeman, L. (1977) · Sociometry · doi:10.2307/3033543 · Cited by 10,557
Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,236
Network Flows: Theory, Algorithms, and Applications.
Smith, D. K., Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1994) · Journal of the Operational Research Society · doi:10.2307/2583863 · Cited by 8,121
Social Network Analysis: A Handbook.
Pappi, F. U., & Scott, J. P. (1993) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2075047 · Cited by 5,964
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Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
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-citationFortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010
Data clustering: 50 years beyond K-means
Jain, A. K. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.09.011 · Cited by 10,021
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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