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

Abstract

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
Pages1270-1278
Number of pages9
ISBN (Electronic)9781450366755
DOIs
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

Conference

Conference2019 World Wide Web Conference, WWW 2019
CountryUnited States
CitySan Francisco
Period5/13/195/17/19

Keywords

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

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this

Beigi, G., Ranganath, S., & Liu, H. (2019). Signed link prediction with sparse data: The role of personality information. In The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019 (pp. 1270-1278). (The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3308560.3316469

Signed link prediction with sparse data : The role of personality information. / Beigi, Ghazaleh; Ranganath, Suhas; Liu, Huan.

The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019. Association for Computing Machinery, Inc, 2019. p. 1270-1278 (The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019).

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

Beigi, G, Ranganath, S & Liu, H 2019, Signed link prediction with sparse data: The role of personality information. in The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019. The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019, Association for Computing Machinery, Inc, pp. 1270-1278, 2019 World Wide Web Conference, WWW 2019, San Francisco, United States, 5/13/19. https://doi.org/10.1145/3308560.3316469
Beigi G, Ranganath S, Liu H. Signed link prediction with sparse data: The role of personality information. In The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019. Association for Computing Machinery, Inc. 2019. p. 1270-1278. (The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019). https://doi.org/10.1145/3308560.3316469
Beigi, Ghazaleh ; Ranganath, Suhas ; Liu, Huan. / Signed link prediction with sparse data : The role of personality information. The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019. Association for Computing Machinery, Inc, 2019. pp. 1270-1278 (The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019).
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