Link label prediction in signed social networks

Priyanka Agrawal, Vikas K. Garg, Ramasuri Narayanam

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

42 Citations (Scopus)

Abstract

Online social networks continue to witness a tremendous growth both in terms of the number of registered users and their mutual interactions. In this paper, we focus on online signed social networks where positive interactions among the users signify friendship or approval, whereas negative interactions indicate antagonism or disapproval. We introduce a novel problem which we call the link label prediction problem: Given the information about signs of certain links in a social network, we want to learn the nature of relationships that exist among the users by predicting the sign, positive or negative, of the remaining links. We propose a matrix factorization based technique MF-LiSP that exhibits strong generalization guarantees. We also investigate the applicability of in this setting. Our experiments on Wiki-Vote, Epinions and Slashdot data sets strongly corroborate the efficacy of these approaches.

Original languageEnglish
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Pages2591-2597
Number of pages7
Publication statusPublished - 2013
MoE publication typeA4 Article in a conference publication
EventInternational Joint Conference of Artificial Intelligence - Beijing, China
Duration: 3 Aug 20139 Aug 2013
Conference number: 23

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

ConferenceInternational Joint Conference of Artificial Intelligence
Abbreviated titleIJCAI
Country/TerritoryChina
CityBeijing
Period03/08/201309/08/2013

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