The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A framework for community identification in dynamic social networks
Abstract
We propose frameworks and algorithms for identifying communities in social networks that change over time. Communities are intuitively characterized as "unusually densely knit" subsets of a social network. This notion becomes more problematic if the social interactions change over time. Aggregating social networks over time can radically misrepresent the existing and changing community structure. Instead, we propose an optimization-based approach for modeling dynamic community structure. We prove that finding the most explanatory community structure is NP-hard and APX-hard, and propose algorithms based on dynamic programming, exhaustive search, maximum matching, and greedy heuristics. We demonstrate empirically that the heuristics trace developments of community structure accurately for several synthetic and real-world examples.
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Cites
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
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
Cited by
Holme, P., & Saramäki, J. (2012) · Physics Reports · doi:10.1016/j.physrep.2012.03.001 · Cited by 2,736
Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
Modern temporal network theory: a colloquium
Holme, P. (2015) · The European Physical Journal B · doi:10.1140/epjb/e2015-60657-4 · Cited by 734
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
Dynamic Network Embedding by Modeling Triadic Closure Process
Zhou, L., Yang, Y., Ren, X., Wu, F., & Zhuang, Y. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11257 · Cited by 473
Metadata
- Authors
- Tantipathananandh, C., Berger-Wolf, T., & Kempe, D.
- Venue
- Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2007-08-12
- Pages
- 717-726
- OpenAlex
- W2122278421
- Cited by
- 443
- References
- 2
- FWCI
- 33.03
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Tantipathananandh, C., Berger-Wolf, T., & Kempe, D. (2007). A framework for community identification in dynamic social networks. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1281192.1281269
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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.
