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Detecting Overlapping Community Structure With Node Influence
2019
IEEE Access
Discovering the underlying overlapping community divisions can guide us in better exploring and predicting the structure and properties of the network. However, a large number of existing methods assume that nodes belong only to a single community. In this paper, we designed a posterior probabilistic prediction model under the Mixed-Membership Stochastic Blockmodel framework to accurately detect the overlapping community structure that exists in the network. In order to capture the degree of
doi:10.1109/access.2019.2955161
fatcat:m73bei6ezvgkvntpimc7qppdqm