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
Volume2015-January
ISBN (Electronic)9781577357384
StatePublished - Jan 1 2015
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: Jul 25 2015Jul 31 2015

Other

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

Fingerprint

Electric network analysis
Polynomials

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Szczepański, P. L., Barcz, A., Michalak, T. P., & Rahwan, T. (2015). The game-theoretic interaction index on social networks with applications to link prediction and community detection. In M. Wooldridge, & Q. Yang (Eds.), IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence (Vol. 2015-January, pp. 638-644). International Joint Conferences on Artificial Intelligence.

The game-theoretic interaction index on social networks with applications to link prediction and community detection. / Szczepański, Piotr L.; Barcz, Aleksy; Michalak, Tomasz P.; Rahwan, Talal.

IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. ed. / Michael Wooldridge; Qiang Yang. Vol. 2015-January International Joint Conferences on Artificial Intelligence, 2015. p. 638-644.

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

Szczepański, PL, Barcz, A, Michalak, TP & Rahwan, T 2015, The game-theoretic interaction index on social networks with applications to link prediction and community detection. in M Wooldridge & Q Yang (eds), IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. vol. 2015-January, International Joint Conferences on Artificial Intelligence, pp. 638-644, 24th International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, 7/25/15.
Szczepański PL, Barcz A, Michalak TP, Rahwan T. The game-theoretic interaction index on social networks with applications to link prediction and community detection. In Wooldridge M, Yang Q, editors, IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. Vol. 2015-January. International Joint Conferences on Artificial Intelligence. 2015. p. 638-644
Szczepański, Piotr L. ; Barcz, Aleksy ; Michalak, Tomasz P. ; Rahwan, Talal. / The game-theoretic interaction index on social networks with applications to link prediction and community detection. IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. editor / Michael Wooldridge ; Qiang Yang. Vol. 2015-January International Joint Conferences on Artificial Intelligence, 2015. pp. 638-644
@inproceedings{40dc60e56cc04bd295b14d3d96556341,
title = "The game-theoretic interaction index on social networks with applications to link prediction and community detection",
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.",
author = "Szczepański, {Piotr L.} and Aleksy Barcz and Michalak, {Tomasz P.} and Talal Rahwan",
year = "2015",
month = "1",
day = "1",
language = "English (US)",
volume = "2015-January",
pages = "638--644",
editor = "Michael Wooldridge and Qiang Yang",
booktitle = "IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",

}

TY - GEN

T1 - The game-theoretic interaction index on social networks with applications to link prediction and community detection

AU - Szczepański, Piotr L.

AU - Barcz, Aleksy

AU - Michalak, Tomasz P.

AU - Rahwan, Talal

PY - 2015/1/1

Y1 - 2015/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84949815327&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84949815327&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84949815327

VL - 2015-January

SP - 638

EP - 644

BT - IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence

A2 - Wooldridge, Michael

A2 - Yang, Qiang

PB - International Joint Conferences on Artificial Intelligence

ER -