基于图嵌入和多标签传播的重叠社区检测算法.

In order to further optimize the overlapping community detection algorithm, this paper proposed a new definition of node importance based on degree and node clustering coefficient, and the nodes were updated in descending order of node importance, and the node update strategy was fixed to improve the stability of community detection. On this basis, this paper proposed an OCD-GEMPA. The algorithm combined the node2vec model to represent the nodes in a low-dimensional vector, constructed a matrix of weight values between nodes, calculated the label attribution coefficient according to the weight values, and selected labels accordingly, avoiding the problem of random selection. Experimental verification of the algorithm on real data sets and synthetic data sets shows that compared to other overlapping community detection algorithms, the OCD-GEMPA algorithm has significant improvements in both EQ and NMI indicators, with better accuracy and stability. [ABSTRACT FROM AUTHOR]