Abstract: The real world is considered to be composed of many different complex systems.In order to model and analyze the hidden rules and functions among individuals in complex systems,the complex system may be abstracted as a complex network composed of nodes and edges.Mining community structures in complex networks has important theoretical significance and practical value in content recommendation,behavior prediction and disease spread.With the continuous changes of individuals in complex systems,overlapping nodes appear among multiple communities.How to effectively and accurately mine the overlapping nodes in communities has brought some challenges.In order to effectively detect the overlapping nodes in the community,an overlapping community detection method based on rough sets and distance dynamic model (OCDRDD) is proposed in this paper.First of all,according to the topology of the network,it selects K core nodes by combining node degree centrality and distance,then initializes the approximation sets and the boundary region of the community according to the distance ratio relationship.Combined with the distance dynamic model,the distances between boundary region nodes and the lower approximation set nodes are changed iteratively.During each iteration,boundary region nodes that conform to the defined distance ratio relationship are classified into the lower approximation of the community,and the boundary region nodes are reduced until the optimal overlapping community structure is found.Finally,the “pseudo” overlapping nodes are processed according to the two rules defined in this paper.NMI and overlapping module degree EQ are taken as evaluation indexes on real network datasets and LFR Benchmark artificial network datasets.The OCDRDD method is compared with other typical community detection methods in recent years both on real network datasets and LFR Benchmark artificial network datasets.The experimental results show that OCDRDD method is better than other community detection algorithms on the whole.The results show that the proposed algorithm is effective and feasible.

Key words: Boundary region, Distance dynamic model, Overlapping community detection, Rough set