The game-theoretic interaction index on social networks with applications to link prediction and community detection

Piotr L. Szczepański, Aleksy Barcz, Tomasz P. Michalak, Talal Rahwan

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

Abstract

Measuring similarity between nodes has been an issue of extensive research in the social network analysis literature. In this paper, we construct a new measure of similarity between nodes based on the game-theoretic interaction index (Grabisch and Roubens, 1997). Despite the fact that, in general, this index is computationally challenging, we show that in our network application it can be computed in polynomial time. We test our measure on two important problems, namely link prediction and community detection, given several real-life networks. We show that, for the majority of those networks, our measure outperforms other local similarity measures from the literature.

Original languageEnglish (US)
Title of host publicationIJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
EditorsMichael Wooldridge, Qiang Yang
PublisherInternational Joint Conferences on Artificial Intelligence
Pages638-644
Number of pages7
ISBN (Electronic)9781577357384
StatePublished - 2015
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: Jul 25 2015Jul 31 2015

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2015-January
ISSN (Print)1045-0823

Other

Other24th International Joint Conference on Artificial Intelligence, IJCAI 2015
CountryArgentina
CityBuenos Aires
Period7/25/157/31/15

ASJC Scopus subject areas

  • Artificial Intelligence

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