Abstract

In this paper, we propose a new algorithm, called STRICLUSTER, to find tri-clusters from signed 3-partite graphs. The dataset contains three different types of nodes. Hyperedges connecting three nodes from three different partitions represent either positive or negative relations among those nodes. The aim of our algorithm is to find clusters with strong positive relations among its nodes. Moreover, negative relations up to a certain threshold is also allowed. Also, the clusters can have no overlapping hyperedges. We show the effectiveness of our algorithm via several experiments.

Original languageEnglish (US)
Title of host publicationProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages945-948
Number of pages4
ISBN (Electronic)9781509028467
DOIs
StatePublished - Nov 21 2016
Event2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States
Duration: Aug 18 2016Aug 21 2016

Other

Other2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
CountryUnited States
CitySan Francisco
Period8/18/168/21/16

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Sociology and Political Science
  • Communication

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    Koc, S. S., Toroslu, I. H., & Davulcu, H. (2016). Co-clustering signed 3-partite graphs. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 (pp. 945-948). [7752353] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2016.7752353