2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2012)
Aug. 26, 2012 to Aug. 29, 2012
P. J. McSweeney , Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA
K. Mehrotra , Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA
J. C. Oh , Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA
The mainstream approach for community detection focuses on the optimization of a metric that measures the quality of a partition over a given network. Optimizing a global metric is akin to community assignment by a centralized decision maker. In liu of global optimization, we treat each node as a player in a hedonic game and focus on their ability to form fair and stable community structures. Application on real-world networks and a well-known benchmark demonstrates that our approach produces better results than modularity optimization.
Communities, Nash equilibrium, Games, Optimization, Measurement, Context
J. C. Oh, K. Mehrotra and P. J. McSweeney, "A Game Theoretic Framework for Community Detection," 2012 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining(ASONAM), Istanbul, 2012, pp. 227-234.