The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
Open accessAbstract
Many complex networks display a mesoscopic structure with groups of nodes sharing many links with the other nodes in their group and comparatively few with nodes of different groups. This feature is known as community structure and encodes precious information about the organization and the function of the nodes. Many algorithms have been proposed but it is not yet clear how they should be tested. Recently we have proposed a general class of undirected and unweighted benchmark graphs, with heterogeneous distributions of node degree and community size. An increasing attention has been recently devoted to develop algorithms able to consider the direction and the weight of the links, which require suitable benchmark graphs for testing. In this paper we extend the basic ideas behind our previous benchmark to generate directed and weighted networks with built-in community structure. We also consider the possibility that nodes belong to more communities, a feature occurring in real systems, such as social networks. As a practical application, we show how modularity optimization performs on our benchmark.
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Citations
Cites
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Self-citationUncovering the overlapping community structure of complex networks in nature and society
Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005) · Nature · doi:10.1038/nature03607 · Cited by 5,373
Functional cartography of complex metabolic networks
Guimerà, R., & Nunes Amaral, L. A. (2005) · Nature · doi:10.1038/nature03288 · Cited by 3,983
Resolution limit in community detection
Fortunato, S., & Barthélemy, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605965104 · Cited by 3,024
Self-citation
Cited by
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Self-citationCommunity 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
Self-citationDisease-associated astrocytes in Alzheimer’s disease and aging
Habib, N., McCabe, C., Medina, S., Varshavsky, M., Kitsberg, D., Dvir-Szternfeld, R., Green, G., Dionne, D., Nguyen, L., Marshall, J. L., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0624-8 · Cited by 1,009
Tracking the Evolution of Communities in Dynamic Social Networks
Greene, D., Doyle, D., & Cunningham, P. (2010) · 2010 International Conference on Advances in Social Networks Analysis and Mining · doi:10.1109/asonam.2010.17 · Cited by 537
Community detection in social networks
Bedi, P., & Sharma, C. (2016) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1178 · Cited by 346
Metadata
- Authors
- Lancichinetti, A., & Fortunato, S.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2009-07-31
- Volume
- 80
- Issue
- 1
- Pages
- 016118-016118
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W1991408655
- Cited by
- 1,009
- References
- 4
- FWCI
- 25.01
- Type
- journal article
- Language
- en
- Open access
- green
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