Social role clustering with topic model

Jie Bai, Linjing Li, Dajun Zeng, Junjie Lin

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

1 Scopus citations


In this paper, we propose a new role analyzing paradigm for social networks enlightened by topic modeling, which can be adopted as a primitive building block in various security related tasks, such as hidden community finding, important person recognizing and so on. We first present the social network under analyzing as a heterogeneous network constructed by both the users and the subjects discussed among them. We then view this network in a Bag-of-Users schema, which mimics its classical Bag-of-Words counterpart. In this schema, the subjects discussed are treated as 'documents' while the users are treated as 'words' which construct the 'documents'. Based on this novel presentation, we finally apply topic modeling technology to perform the social role clustering. Experiments on a practical security-related social network dataset prove the effectiveness of our approach.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Intelligence and Security Informatics
Subtitle of host publicationCybersecurity and Big Data, ISI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages3
ISBN (Electronic)9781509038657
StatePublished - Nov 15 2016
Event14th IEEE International Conference on Intelligence and Security Informatics, ISI 2015 - Tucson, United States
Duration: Sep 28 2016Sep 30 2016


Other14th IEEE International Conference on Intelligence and Security Informatics, ISI 2015
CountryUnited States


  • hidden community
  • network structure mining
  • social network
  • social role
  • topic model

ASJC Scopus subject areas

  • Information Systems
  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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