Abstract
Most real-world social networks are usually dynamic (evolve over time), thus communities are constantly changing in memberships. In this paper, an incremental approach based on the coalition formation game theory to identify communities in dynamic social networks is proposed, where the community evolution is modeled as the problem of transformations of stable coalition structures. The proposed approach adaptively update communities from the previous known structures and the changes of topological structure of a network, rather than re-computing in the snapshots of the network at different time steps, such that the computational cost and processing time can be significantly reduced. Experiments have been conducted to evaluate the effectiveness of the proposed approach.
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Acknowledgement
This research was supported by the National Natural Science Foundation of China (61762090, 61262069, 61472346, and 61662086), The Natural Science Foundation of Yunnan Province (2016FA026, 2015FB114), the Project of Innovative Research Team of Yunnan Province, and Program for Innovation Research Team (in Science and Technology) in University of Yunnan Province (IRTSTYN).
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Xiao, Q., Yang, P., Zhou, L., Wang, L. (2018). An Incremental Approach Based on the Coalition Formation Game Theory for Identifying Communities in Dynamic Social Networks. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_35
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DOI: https://doi.org/10.1007/978-3-319-99365-2_35
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