The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Fast algorithm for detecting community structure in networks
Open accessAbstract
Many networks display community structure--groups of vertices within which connections are dense but between which they are sparser--and sensitive computer algorithms have in recent years been developed for detecting this structure. These algorithms, however, are computationally demanding, which limits their application to small networks. Here we describe an algorithm which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms. We give several example applications, including one to a collaboration network of more than 50,000 physicists.
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Cites
Finding 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-citationDorogovtsev, S. N., & Mendes, J. F. F. (2003) · doi:10.1093/acprof:oso/9780198515906.001.0001 · Cited by 1,563
The emergent properties of a dolphin social network
Lusseau, D. (2003) · Proceedings of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rsbl.2003.0057 · Cited by 844
Research works printed but not linked: 15
- 10.1038/35065725 · DOI · Paper page
- 10.1103/RevModPhys.74.47 · DOI · Paper page
- 10.1103/RevModPhys.74.47 · DOI · Paper page
- 10.1073/pnas.122653799 · DOI · Paper page
- 10.1073/pnas.0307740100 · DOI · Paper page
- 10.1093/bioinformatics/btg033 · DOI · Paper page
- 10.1103/PhysRevE.68.065103 · DOI · Paper page
- J. R. Tyler, 2003, Proceedings of the First International Conference on Communities and Technologies
- P. Gleiser, 2003, Adv. Complex Syst., 6, 565, 10.1142/S0219525903001067 · DOI · Paper page
- 10.1007/s100510050359 · DOI · Paper page
- 10.1073/pnas.021544898 · doi:10.1073/pnas.021544898 · Paper page
- 10.1126/science.1067014 · DOI · Paper page
- B. Everitt, 1974, Cluster Analysis
- J. Scott, 2000, Social Network Analysis: A Handbook, 10.1177/0038038588022001007 · DOI · Paper page
- W. W. Zachary, 1977, J. Anthropol. Res., 33, 452, 10.1086/jar.33.4.3629752 · DOI · Paper page
Cited by
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-citationComplex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
Fortunato, 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
Metadata
- Authors
- Newman, M. E. J.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2004-06-18
- Volume
- 69
- Issue
- 6
- Pages
- 066133-066133
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W2089458547
- Cited by
- 5,406
- References
- 3
- FWCI
- 52.31
- Type
- journal article
- Language
- en
- Open access
- green
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