Abstract

Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we investigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework.

Original languageEnglish (US)
Title of host publicationWSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages87-96
Number of pages10
ISBN (Print)9781450333177
DOIs
StatePublished - Feb 2 2015
Event8th ACM International Conference on Web Search and Data Mining, WSDM 2015 - Shanghai, China
Duration: Jan 31 2015Feb 6 2015

Other

Other8th ACM International Conference on Web Search and Data Mining, WSDM 2015
CountryChina
CityShanghai
Period1/31/152/6/15

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Electric network analysis
Availability
Experiments

Keywords

  • Negative link prediction
  • Negative links
  • Signed social networks
  • Social media

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Tang, J., Chang, S., Aggarwal, C., & Liu, H. (2015). Negative link prediction in social media. In WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining (pp. 87-96). Association for Computing Machinery, Inc. https://doi.org/10.1145/2684822.2685295

Negative link prediction in social media. / Tang, Jiliang; Chang, Shiyu; Aggarwal, Charu; Liu, Huan.

WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc, 2015. p. 87-96.

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

Tang, J, Chang, S, Aggarwal, C & Liu, H 2015, Negative link prediction in social media. in WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc, pp. 87-96, 8th ACM International Conference on Web Search and Data Mining, WSDM 2015, Shanghai, China, 1/31/15. https://doi.org/10.1145/2684822.2685295
Tang J, Chang S, Aggarwal C, Liu H. Negative link prediction in social media. In WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc. 2015. p. 87-96 https://doi.org/10.1145/2684822.2685295
Tang, Jiliang ; Chang, Shiyu ; Aggarwal, Charu ; Liu, Huan. / Negative link prediction in social media. WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc, 2015. pp. 87-96
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