The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Clustering attributed graphs: Models, measures and methods
Open accessAbstract
Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing, and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.
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Cites
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557 · cited 3×
“For example, structural properties of social systems can be measured by representing individuals and their relationships as graphs and computing the centrality or prestige of their nodes (Wasserman & Faust, 1994).”
BackgroundFinding 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
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Uncovering 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
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
Graphs over time: densification laws, shrinking diameters and possible explanations
Leskovec, J., Kleinberg, J., & Faloutsos, C. (2005) · Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining · doi:10.1145/1081870.1081893 · Cited by 2,281
Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010) · Science · doi:10.1126/science.1184819 · Cited by 2,123
Cited by
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
Structure and inference in annotated networks
Newman, M. E. J., & Clauset, A. (2016) · Nature Communications · doi:10.1038/ncomms11863 · Cited by 379
Community detection in node-attributed social networks: A survey
Chunaev, P. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100286 · Cited by 300
Spreading Processes in Multilayer Networks
Salehi, M., Sharma, R., Marzolla, M., Magnani, M., Siyari, P., & Montesi, D. (2015) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2015.2425961 · Cited by 255
Self-citationZhang, J., Gong, J., & Barnes, L. (2017) · 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) · doi:10.1109/chase.2017.80 · Cited by 44
Metadata
- Authors
- BOTHOREL, C., CRUZ, J. D., MAGNANI, M., & MICENKOVÁ, B.
- Venue
- Network Science
- Publisher
- Cambridge University Press (CUP)
- Published
- 2015-03-18
- Volume
- 3
- Issue
- 3
- Pages
- 408-444
- ISSN
- 2050-1242 (ISSN-L)
- 2050-1250
- OpenAlex
- W2124536061
- Cited by
- 249
- References
- 8
- FWCI
- 13.47
- Type
- journal article
- Language
- en
- Open access
- green
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