Signed link prediction with sparse data: The role of personality information

Ghazaleh Beigi, Suhas Ranganath, Huan Liu

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

11 Scopus citations


Predicting signed links in social networks often faces the problem of signed link data sparsity, i.e., only a small percentage of signed links are given. The problem is exacerbated when the number of negative links is much smaller than that of positive links. Boosting signed link prediction necessitates additional information to compensate for data sparsity. According to psychology theories, one rich source of such information is user's personality such as optimism and pessimism that can help determine her propensity in establishing positive and negative links. In this study, we investigate how personality information can be obtained, and if personality information can help alleviate the data sparsity problem for signed link prediction. We propose a novel signed link prediction model that enables empirical exploration of user personality via social media data. We evaluate our proposed model on two datasets of real-world signed link networks. The results demonstrate the complementary role of personality information in the signed link prediction problem. Experimental results also indicate the effectiveness of different levels of personality information for signed link data sparsity problem.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019
PublisherAssociation for Computing Machinery, Inc
Number of pages9
ISBN (Electronic)9781450366755
StatePublished - May 13 2019
Event2019 World Wide Web Conference, WWW 2019 - San Francisco, United States
Duration: May 13 2019May 17 2019

Publication series

NameThe Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019


Conference2019 World Wide Web Conference, WWW 2019
Country/TerritoryUnited States
CitySan Francisco


  • Data Sparsity
  • Optimism
  • Personality Information
  • Pessimism
  • Signed Link Prediction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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