TY - GEN
T1 - A game theoretic framework for community detection
AU - McSweeney, Patrick J.
AU - Mehrotra, Kishan
AU - Oh, Jae C.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84874230938&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874230938&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2012.47
DO - 10.1109/ASONAM.2012.47
M3 - Conference contribution
AN - SCOPUS:84874230938
SN - 9780769547992
T3 - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
SP - 227
EP - 234
BT - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
T2 - 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
Y2 - 26 August 2012 through 29 August 2012
ER -