Overlapping community detection via preferential learning model

Overlapping community detection has become one of the most important tasks in network analysis because it can better reflect the characteristics of the real network structure. Research on overlapping communities detection cannot only promote the study of network functions but also bring insight into a deep understanding of the network topology. In this paper, we get inspiration from learning behaviors and information exchanges in the real world, and propose a dynamic relationship-based preference learning model applied to dynamic systems. We apply this model to the label propagation algorithm and present an overlapping community detection algorithm based on Preferential Learning and Label Propagation Algorithm, called PLPA. The algorithm regards the network as a dynamic system. Each node selects the learning target to update its own label according to the degree of preference to its neighbor nodes. With learning, the information in the system will finally reach a steady state. We consider nodes that have the same label belonging to the same community, so that the overlapping community structure in the network will be separated. In the experiments, we verified the performance of our algorithm through real-world and synthetic networks. Results show that PLPA not only has better performance than many state-of-the-art algorithms on most data sets, but it is also more applicable to some networks with ambiguous community structure, especially sparse networks.