Detecting Overlapping Community Structure With Node Influence

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 nodes that exhibit heterogeneous characteristics in the network, the model takes into account the influence of the nodes. In addition, we developed a non-conjugated stochastic variational inference to deduce the link probability prediction model with node influence. The key strategy is to use the mean-domain variational family with variable distribution to approximate the posterior community strengthen and node influence distribution in the prediction model. We compared the performance of this model with the previous algorithm models on computer-generated and real-world networks and found that it gives better results, especially when the heterogeneity of the network is very serious. In general, the combination of node influence and link probabilistic predictive model provides a new idea for us to use a statistical model to explore large-scale overlapping networks.

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