Improved louvain method for directed networks

Abstract

Existing studies about community detection mainly focus on undirected networks. However, research results on detecting community structure in directed networks are less extensive and less systematic. The Louvain Method is one of the best algorithms for community detection in undirected networks. In this study, an algorithm was proposed to detect community structure in mass directed networks. First, the definition for modularity of directed networks based on the community connection matrix was proposed. Second, equations to calculate modularity gain in directed networks were derived. Finally, based on the idea of Louvain Method, an algorithm to detect community in directed networks was proposed. Relevant experiments show that not only does the algorithm have obvious advantages both in run-time and accuracy of community discovery results, but it can also obtain multi-granularity community structure that could reflect the self-similarity characteristics and hierarchical characteristics of complex networks. Experimental results indicate the algorithm is excellent in detecting community structure in mass directed networks.