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Robust Detection of Communities with Multi-semantics in Large Attributed Networks

  • Di Jin
  • Ziyang Liu
  • Dongxiao He
  • Bogdan Gabrys
  • Katarzyna Musial
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

Abstract

In this paper, we are interested in how to explore and utilize the relationship between network communities and semantic topics in order to find the strong explanatory communities robustly. First, the relationship between communities and topics displays different situations. For example, from the viewpoint of semantic mapping, their relationship can be one-to-one, one-to-many or many-to-one. But from the standpoint of underlying community structures, the relationship can be consistent, partially consistent or completely inconsistent. Second, it will be helpful to not only find communities more precise but also reveal the communities’ semantics that shows the relationship between communities and topics. To better describe this relationship, we introduce the transition probability which is an important concept in Markov chain into a well-designed nonnegative matrix factorization framework. This new transition probability matrix with a suitable prior which plays the role of depicting the relationship between communities and topics can perform well in this task. To illustrate the effectiveness of the proposed new approach, we conduct some experiments on both synthetic and real networks. The results show that our new method is superior to baselines in accuracy. We finally conduct a case study analysis to validate the new method’s strong interpretability to detected communities.

Keywords

Community detection Social networks Semantics Transition probability Nonnegative matrix factorization 

Notes

Acknowledgment

This work was supported by the National Key R&D Program of China (2017YFC0820106), the Natural Science Foundation of China (61502334, 61772361, 61673293) and the Elite Scholar Program of Tianjin University (2017XRG-0016).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Advanced Analytics Institute, School of Software, Faculty of Engineering and ITUniversity of Technology SydneyUltimoAustralia

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