The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Finding local community structure in networks
Open accessAbstract
Although the inference of global community structure in networks has recently become a topic of great interest in the physics community, all such algorithms require that the graph be completely known. Here, we define both a measure of local community structure and an algorithm that infers the hierarchy of communities that enclose a given vertex by exploring the graph one vertex at a time. This algorithm runs in time O(k2d) for general graphs when d is the mean degree and k is the number of vertices to be explored. For graphs where exploring a new vertex is time consuming, the running time is linear, O(k). We show that on computer-generated graphs the average behavior of this technique approximates that of algorithms that require global knowledge. As an application, we use this algorithm to extract meaningful local clustering information in the large recommender network of an online retailer.
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Cites
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
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
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
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
Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,302
Finding 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-citationBollobás, B. (1985) · Cited by 6,196
(2017) · The SAGE Encyclopedia of Communication Research Methods · doi:10.4135/9781483381411.n575 · Cited by 6,008
Social Network Analysis: A Handbook.
Pappi, F. U., & Scott, J. (1993) · Contemporary Sociology · doi:10.2307/2075047 · Cited by 5,969
Cited by
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Community detection in networks: a user guide
Fortunato, S., & Hric, D. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.09.002 · Cited by 2,086
Schaeffer, S. E. (2007) · Computer Science Review · doi:10.1016/j.cosrev.2007.05.001 · Cited by 1,639
Performance of modularity maximization in practical contexts
Good, B. H., de Montjoye, Y.-A., & Clauset, A. (2010) · Physical Review E · doi:10.1103/physreve.81.046106 · Cited by 937
Self-citationSocial big data: recent achievements and new challenges
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016) · Information Fusion · doi:10.1016/j.inffus.2015.08.005 · Cited by 846
Metadata
- Authors
- Clauset, A.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2005-08-29
- Volume
- 72
- Issue
- 2
- Pages
- 026132-026132
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W2095072199
- Cited by
- 788
- References
- 30
- FWCI
- 9.91
- Type
- journal article
- Language
- en
- Open access
- green
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