Abstract
The increasing popularity of online social networks (OSNs) has attracted significant attention from the research community. One of the significant tasks of analyzing structure is to detect communities within these OSN. Nodes within a community tend to be densely connected in comparison with nodes outside the community. Community detection is a challenging task in the field of social networks (SNs). Graph model is used to represent OSN where the nodes are representative of actors and the edges represent the interactions between these actors. A novel community detection algorithm named SCD is proposed in this paper. In the underlying graph, nodes are modeled as rational players where each player wants to maximize its Shapley value in terms of interactions. Extensive experiments are conducted on real-world datasets to establish the efficiency of the proposed approach.
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Ahmad, A., Ahmad, T., Bhatt, A., Siddiqui, S. (2019). A Shapley Value Approach for Community Detection in Social Network. In: Jain, L., E. Balas, V., Johri, P. (eds) Data and Communication Networks. Advances in Intelligent Systems and Computing, vol 847. Springer, Singapore. https://doi.org/10.1007/978-981-13-2254-9_22
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DOI: https://doi.org/10.1007/978-981-13-2254-9_22
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