A complex network community detection algorithm based on random walk and label propagation

The community structure is proving to have a very important role in the understanding of complex networks, but discovering them remains a very difficult problem despite the existence of several methods. In this article, we propose a novel algorithm for discovering communities in complex networks based on a modified random walk (RW) and label propagation algorithm (LPA). First, we calculate the similarity between nodes based on the new formula of RW. Then, the labels are propagated by the obtained similarity of the first step using LPA. Finally, the third step will be a new measure to find the optimal partitioning of communities. Experimental results obtained on several real and synthetic networks reveal that our algorithm outperforms existing methods in finding communities.

Automated summary

In this article, we propose a novel algorithm based on a modified random walk and label propagation algorithm for discovering communities in complex networks. Experimental results show that our algorithm outperforms existing methods.