Abstract

Node classification in social networks has been proven to be useful in many real-world applications. The vast majority of existing algorithms focus on unsigned social networks (or social networks with only positive links), while little work exists for signed social networks. It is evident from recent developments in signed social network analysis that negative links have added value over positive links. Therefore, the incorporation of negative links has the potential to benefit various analytical tasks. In this paper, we study the novel problem of node classification in signed social networks. We provide a principled way to mathematically model positive and negative links simultaneously and propose a novel framework NCSSN for node classification in signed social networks. Experimental results on real-world signed social network datasets demonstrate the effectiveness of the proposed framework NCSSN. Further experiments are conducted to gain a deeper understanding of the importance of negative links for NCSSN.

Original languageEnglish (US)
Title of host publication16th SIAM International Conference on Data Mining 2016, SDM 2016
EditorsSanjay Chawla Venkatasubramanian, Wagner Meira
PublisherSociety for Industrial and Applied Mathematics Publications
Pages54-62
Number of pages9
ISBN (Electronic)9781510828117
DOIs
StatePublished - Jan 1 2016
Event16th SIAM International Conference on Data Mining 2016, SDM 2016 - Miami, United States
Duration: May 5 2016May 7 2016

Publication series

Name16th SIAM International Conference on Data Mining 2016, SDM 2016

Other

Other16th SIAM International Conference on Data Mining 2016, SDM 2016
CountryUnited States
CityMiami
Period5/5/165/7/16

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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  • Cite this

    Tang, J., Aggarwal, C., & Liu, H. (2016). Node classification in signed social networks. In S. C. Venkatasubramanian, & W. Meira (Eds.), 16th SIAM International Conference on Data Mining 2016, SDM 2016 (pp. 54-62). (16th SIAM International Conference on Data Mining 2016, SDM 2016). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611974348.7