The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Community evolution in dynamic multi-mode networks
Abstract
A multi-mode network typically consists of multiple heterogeneous social actors among which various types of interactions could occur. Identifying communities in a multi-mode network can help understand the structural properties of the network, address the data shortage and unbalanced problems, and assist tasks like targeted marketing and finding influential actors within or between groups. In general, a network and the membership of groups often evolve gradually. In a dynamic multi-mode network, both actor membership and interactions can evolve, which poses a challenging problem of identifying community evolution. In this work, we try to address this issue by employing the temporal information to analyze a multi-mode network. A spectral framework and its scalability issue are carefully studied. Experiments on both synthetic data and real-world large scale networks demonstrate the efficacy of our algorithm and suggest its generality in solving problems with complex relationships.
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Cites
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
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
Cited by
A survey of heterogeneous information network analysis
Shi, C., Li, Y., Zhang, J., Sun, Y., & Yu, P. S. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2598561 · Cited by 1,112
Self-citationRelational learning via latent social dimensions
Tang, L., & Liu, H. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557109 · Cited by 799
Self-citationTracking 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
A classification for community discovery methods in complex networks
Coscia, M., Giannotti, F., & Pedreschi, D. (2011) · Statistical Analysis and Data Mining: The ASA Data Science Journal · doi:10.1002/sam.10133 · Cited by 353
Evolutionary Network Analysis: A Survey
Aggarwal, C., & Subbian, K. (2014) · ACM Computing Surveys · doi:10.1145/2601412 · Cited by 313
Metadata
- Authors
- Tang, L., Liu, H., Zhang, J., & Nazeri, Z.
- Venue
- Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2008-08-24
- Pages
- 677-685
- OpenAlex
- W2050153214
- Cited by
- 245
- References
- 2
- FWCI
- 20.46
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Tang, L., Liu, H., Zhang, J., & Nazeri, Z. (2008). Community evolution in dynamic multi-mode networks. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1401890.1401972
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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