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Authors: Maryam Sorkhi ; Hamidreza Alvari ; Sattar Hashemi and Ali Hamzeh

Affiliation: Shiraz University, Iran, Islamic Republic of

ISBN: 978-989-8565-29-7

Keyword(s): Game-Theoretic Framework, Nash Equilibrium, Team of Experts, Social Networks.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems ; Web Mining

Abstract: Discovering teams of experts in social networks has been receiving the increasing attentions recently. These teams are often formed when a given specific task should be accomplished by the collaboration and the communication of the small number of connected experts and with the minimum communication cost. In this study we propose a game theoretic framework to find top-k teams satisfying such conditions. The importance of finding top-k teams is revealed when the experts of the best discovered team do not have an incentive to work together for any reason and hence we must refer to the next found teams. Finally, the local Nash equilibrium corresponding to the game is reached when all of the teams are formed. The experimental results on DBLP co-authorship graph show the effectiveness and efficiency of the proposed method.

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Paper citation in several formats:
Sorkhi, M.; Alvari, H.; Hashemi, S. and Hamzeh, A. (2012). A Game-Theoretic Framework to Identify Top-K Teams in Social Networks.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 252-257. DOI: 10.5220/0004142302520257

@conference{kdir12,
author={Maryam Sorkhi. and Hamidreza Alvari. and Sattar Hashemi. and Ali Hamzeh.},
title={A Game-Theoretic Framework to Identify Top-K Teams in Social Networks},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={252-257},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004142302520257},
isbn={978-989-8565-29-7},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - A Game-Theoretic Framework to Identify Top-K Teams in Social Networks
SN - 978-989-8565-29-7
AU - Sorkhi, M.
AU - Alvari, H.
AU - Hashemi, S.
AU - Hamzeh, A.
PY - 2012
SP - 252
EP - 257
DO - 10.5220/0004142302520257

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