关键词: 对称社会距离, 随机行走, 可能性C均值算法, 准确度指标

Abstract: In the process of community discovery, it firstly starts a random walk from a node, calculates the symmetrical social distance between two nodes, and uses this distance to analyze the correlation between two user nodes. In the social network, there is a phenomenon of non-uniformity. Some individuals are very dense, while others are very sparse. Therefore, the virtual community needs to be excavated with specific community discovery technology. However, through the accuracy index of virtual community algorithm evaluation, it is found that for the data with large data volume and strong data stickiness, the clustering algorithm of poly-clustering algorithm（PCM） class effect is not ideal. The PCM algorithm is improved with central constraints, the new clustering algorithm is more suitable for the existence of some data missing or there is a large number of noise, the exception point of the real network data set. Experiments are carried out to verify the accuracy of the real data set.

Key words: symmertrical social distance, random walk, Possibilistic C-Means（PCM） algorithm, accuracy of indicators