A Game-Theoretic Framework to Identify Top-K Teams in Social Networks

Maryam Sorkhi, Hamidreza Alvari, Sattar Hashemi, Ali Hamzeh

2012

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.

References

  1. Adjeroh, D., Kandaswamy, U, 2007. Game-Theoretic Analysis of Network Community Structure. Vol.3, No.4, pp. 313-325, doi:10.5019/j.ijcir.2007.112.
  2. Alvari, H., Hashemi, S., Hamzeh, A, 2011. Detecting Overlapping Communities in Social Networks by Game Theory and Equivalence Concept. AICI 2011, Part II, LNAI 7003, pp.620 - 630, Springer-Verlag.
  3. Baykasoglu, A., Dereli, T., Das, S., 2007. Project Team Selection Using Fuzzy Optimization Approach. Presented at Cybernetics and Systems, pp.155-185.
  4. Cheatham, M., Cleereman, K., 2006. Application of Social Network Analysis to Collaborative Team Formation. In Proc. of Intl. Symposium on Collaborative Technologies and Systems, pp.306-311.
  5. Chen, S.-J., Lin, L., 2004. Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering. IEEE Transactions on Engineering Management, pp.111-124.
  6. Gaston, M., Simmons, J., Desjardins, M., 2004. Adapting Network Structures for Efficient Team Formation. In Proceedings of the AAAI Fall Symposium on Artificial Multi-agent Learning.
  7. Lappas, T., Liu, K., Terzi, E., 2009. Finding a Team of Experts in Social Networks. In Proc. of ACM Intl. Conference on Knowledge Discovery and Data Mining (KDD'09), pp.467-476.
  8. Lorrain, F., White, H. C., 1971. Structural equivalence of individuals in social networks. The Journal of Mathematical Sociology 1(1): 49-80.
  9. Wi, H., Oh, S., Mun, J., Jung, M., 2009. A Team Formation Model Based on Knowledge and Collaboration. Expert Syst. Appl., vol. 36, pp.9121- 9134.
  10. Zakarian, A., Kusiak, A., 2004. Forming Teams: An Analytical Approach. IIE Transactions, 31:85-97.
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Paper Citation


in Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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