On finding socially tenuous groups for online social networks

Chih Ya Shen, Liang Hao Huang, De Nian Yang, Hong Han Shuai, Wang Chien Lee, Ming Syan Chen

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

7 Scopus citations

Abstract

Existing research on finding social groups mostly focuses on dense subgraphs in social networks. However, finding socially tenuous groups also has many important applications. In this paper, we introduce the notion of k-triangles to measure the tenuity of a group. We then formulate a new research problem, Minimum k-Triangle Disconnected Group (MkTG), to find a socially tenuous group from online social networks. We prove that MkTG is NP-Hard and inapproximable within any ratio in arbitrary graphs but polynomial-time tractable in threshold graphs. Two algorithms, namely TERA and TERA-ADV, are designed to exploit graph-theoretical approaches for solving MkTG on general graphs effectively and efficiently Experimental results on seven real datasets manifest that the proposed algorithms outperform existing approaches in both efficiency and solution quality.

Original languageEnglish (US)
Title of host publicationKDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages415-424
Number of pages10
ISBN (Electronic)9781450348874
DOIs
StatePublished - Aug 13 2017
Event23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada
Duration: Aug 13 2017Aug 17 2017

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
VolumePart F129685

Other

Other23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
CountryCanada
CityHalifax
Period8/13/178/17/17

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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  • Cite this

    Shen, C. Y., Huang, L. H., Yang, D. N., Shuai, H. H., Lee, W. C., & Chen, M. S. (2017). On finding socially tenuous groups for online social networks. In KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 415-424). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F129685). Association for Computing Machinery. https://doi.org/10.1145/3097983.3097995