Detecting and tracking community dynamics in evolutionary networks

Zhengzhang Chen, Kevin A. Wilson, Ye Jin, William Hendrix, Nagiza F. Samatova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

Community structure or clustering is ubiquitous in many evolutionary networks including social networks, biological networks and financial market networks. Detecting and tracking community deviations in evolutionary networks can uncover important and interesting behaviors that are latent if we ignore the dynamic information. In biological networks, for example, a small variation in a gene community may indicate an event, such as gene fusion, gene fission, or gene decay. In contrast to the previous work on detecting communities in static graphs or tracking conserved communities in time-varying graphs, this paper first introduces the concept of community dynamics, and then shows that the baseline approach by enumerating all communities in each graph and comparing all pairs of communities between consecutive graphs is infeasible and impractical. We propose an efficient method for detecting and tracking community dynamics in evolutionary networks by introducing graph representatives and community representatives to avoid generating redundant communities and limit the search space. We measure the performance of the representative-based algorithm by comparison to the baseline algorithm on synthetic networks, and our experiments show that our algorithm achieves a runtime speedup of 11-46. The method has also been applied to two real-world evolutionary networks including Food Web and Enron Email. Significant and informative community dynamics have been detected in both cases.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages318-327
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2010
Event10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - Sydney, NSW, Australia
Duration: 14 Dec 201017 Dec 2010

Other

Other10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
CountryAustralia
CitySydney, NSW
Period14/12/1017/12/10

Fingerprint

Genes
Electronic mail
Fusion reactions
Experiments

Keywords

  • Community detection
  • Community dynamics
  • Evolutionary analysis
  • Social networks

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Chen, Z., Wilson, K. A., Jin, Y., Hendrix, W., & Samatova, N. F. (2010). Detecting and tracking community dynamics in evolutionary networks. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 318-327). [5693316] https://doi.org/10.1109/ICDMW.2010.32

Detecting and tracking community dynamics in evolutionary networks. / Chen, Zhengzhang; Wilson, Kevin A.; Jin, Ye; Hendrix, William; Samatova, Nagiza F.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2010. p. 318-327 5693316.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chen, Z, Wilson, KA, Jin, Y, Hendrix, W & Samatova, NF 2010, Detecting and tracking community dynamics in evolutionary networks. in Proceedings - IEEE International Conference on Data Mining, ICDM., 5693316, pp. 318-327, 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010, Sydney, NSW, Australia, 14/12/10. https://doi.org/10.1109/ICDMW.2010.32
Chen Z, Wilson KA, Jin Y, Hendrix W, Samatova NF. Detecting and tracking community dynamics in evolutionary networks. In Proceedings - IEEE International Conference on Data Mining, ICDM. 2010. p. 318-327. 5693316 https://doi.org/10.1109/ICDMW.2010.32
Chen, Zhengzhang ; Wilson, Kevin A. ; Jin, Ye ; Hendrix, William ; Samatova, Nagiza F. / Detecting and tracking community dynamics in evolutionary networks. Proceedings - IEEE International Conference on Data Mining, ICDM. 2010. pp. 318-327
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