The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A framework for analysis of dynamic social networks
Abstract
Finding patterns of social interaction within a population has wide-ranging applications including: disease modeling, cultural and information transmission, and behavioral ecology. Social interactions are often modeled with networks. A key characteristic of social interactions is their continual change. However, most past analyses of social networks are essentially static in that all information about the time that social interactions take place is discarded. In this paper, we propose a new mathematical and computational framework that enables analysis of dynamic social networks and that explicitly makes use of information about when social interactions occur.
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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
The origin of bursts and heavy tails in human dynamics
Barabási, A.-L. (2005) · Nature · doi:10.1038/nature03459 · Cited by 2,210
Cited by
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
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
Community detection in social networks
Bedi, P., & Sharma, C. (2016) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1178 · Cited by 346
Evolutionary Network Analysis: A Survey
Aggarwal, C., & Subbian, K. (2014) · ACM Computing Surveys · doi:10.1145/2601412 · Cited by 313
Graph pattern matching: from intractable to polynomial time
Fan, W., Li, J., Ma, S., Tang, N., Wu, Y., & Wu, Y. (2010) · Proceedings of the VLDB Endowment · doi:10.14778/1920841.1920878 · Cited by 285
Metadata
- Authors
- Berger-Wolf, T. Y., & Saia, J.
- Venue
- Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2006-08-20
- Pages
- 523-528
- OpenAlex
- W2162691596
- Cited by
- 253
- References
- 3
- FWCI
- 23.51
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Berger-Wolf, T. Y., & Saia, J. (2006). A framework for analysis of dynamic social networks. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1150402.1150462
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
