Optimizing Community Detection Using the Pre-processing of Edge Weighted Based on Random Walk in Networks

In the context of social network becomes more and more complicated and huge, it is difficult to improve the accuracy and performance of existing community detection algorithms only relying on the network topological features. Based on Markov random walk theory, this paper proposes a method of edge weighted pre-processing for optimizing community detection, models community structures how to influence on the complex network behaviors. According to the situation of multiple random walk traverses on the network links, the network edges weight is reset, and makes it as the network topology effective supplementary information to promote the network community structure defuzzification, thus the performance of the existing algorithms is improved for community detection. For a set of typical benchmark computer-generated networks and real-world network data sets, the experimental results show that the pre-processing method can effectively improve the accuracy and efficiency of some existing community detection algorithms.