The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Computing Communities in Large Networks Using Random Walks
Open accessAbstract
Abstract. Dense subgraphs of sparse graphs (communities), which appear in most real-world complex networks, play an important role in many contexts. Computing them however is generally expensive. We propose here a measure of similarities between vertices based on random walks which has several important advantages: it captures well the community structure in a network, it can be computed efficiently, it works at various scales, and it can be used in an agglomerative algorithm to compute efficiently the community structure of a network. We propose such an algorithm which runs in time O(mn 2)andspaceO(n 2)inthe worst case, and in time O(n 2 log n) andspaceO(n 2)inmostreal-world cases (n and m are respectively the number of vertices and edges in the input graph). 1
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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
Hierarchical Organization of Modularity in Metabolic Networks
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224
Defining and identifying communities in networks
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400054101 · Cited by 2,450
Dorogovtsev, S. N., & Mendes, J. F. F. (2003) · doi:10.1093/acprof:oso/9780198515906.001.0001 · Cited by 1,563
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
Network analysis of multivariate data in psychological science
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00055-w · Cited by 1,371
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
Polarization of the vaccination debate on Facebook
Schmidt, A. L., Zollo, F., Scala, A., Betsch, C., & Quattrociocchi, W. (2018) · Vaccine · doi:10.1016/j.vaccine.2018.05.040 · Cited by 403
The triumphs and limitations of computational methods for scRNA-seq
Kharchenko, P. V. (2021) · Nature Methods · doi:10.1038/s41592-021-01171-x · Cited by 321
Metadata
- Authors
- Pons, P., & Latapy, M.
- Venue
- Journal of Graph Algorithms and Applications
- Published
- 2006
- Volume
- 10
- Issue
- 2
- Pages
- 191-218
- ISSN
- 1526-1719 (ISSN-L)
- OpenAlex
- W2033590892
- Cited by
- 1,501
- References
- 4
- FWCI
- 11.35
- Type
- journal article
- Language
- en
- Open access
- diamond
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