Community detection in dynamic social networks: A game-theoretic approach

Community detection in dynamic social networks: A game-theoretic approach. Alvari, H., Hajibagheri, A., & Sukthankar, G. In 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pages 101–107, August, 2014.
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Most real-world social networks are inherently dynamic and composed of communities that are constantly changing in membership. As a result, recent years have witnessed increased attention toward the challenging problem of detecting evolving communities. This paper presents a game-theoretic approach for community detection in dynamic social networks in which each node is treated as a rational agent who periodically chooses from a set of predefined actions in order to maximize its utility function. The community structure of a snapshot emerges after the game reaches Nash equilibrium; the partitions and agent information are then transferred to the next snapshot. An evaluation of our method on two real world dynamic datasets (AS-Internet Routers Graph and AS-Oregon Graph) demonstrates that we are able to report more stable and accurate communities over time compared to the benchmark methods.

@inproceedings{alvari_community_2014,
	title = {Community detection in dynamic social networks: {A} game-theoretic approach},
	shorttitle = {Community detection in dynamic social networks},
	doi = {10.1109/ASONAM.2014.6921567},
	abstract = {Most real-world social networks are inherently dynamic and composed of communities that are constantly changing in membership. As a result, recent years have witnessed increased attention toward the challenging problem of detecting evolving communities. This paper presents a game-theoretic approach for community detection in dynamic social networks in which each node is treated as a rational agent who periodically chooses from a set of predefined actions in order to maximize its utility function. The community structure of a snapshot emerges after the game reaches Nash equilibrium; the partitions and agent information are then transferred to the next snapshot. An evaluation of our method on two real world dynamic datasets (AS-Internet Routers Graph and AS-Oregon Graph) demonstrates that we are able to report more stable and accurate communities over time compared to the benchmark methods.},
	booktitle = {2014 {IEEE}/{ACM} {International} {Conference} on {Advances} in {Social} {Networks} {Analysis} and {Mining} ({ASONAM} 2014)},
	author = {Alvari, H. and Hajibagheri, A. and Sukthankar, G.},
	month = aug,
	year = {2014},
	pages = {101--107},
}

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