A Fast Local Balanced Label Diffusion Algorithm for Community Detection in Social Networks

Abstract

Community detection in large-scale networks is one of the main challenges in social networks analysis. Proposing a fast and accurate algorithm with low time complexity is vital for large-scale networks. In this paper, a fast community detection algorithm based on local balanced label diffusion (LBLD) is proposed. The LBLD algorithm starts with assigning node importance score to each node using a new local similarity measure. After that, top 5% important nodes are selected as initial rough cores to expand communities. In the first step, two neighbor nodes with highest similarity than others receive a same label. In the second step, based on the selected rough cores, the proposed algorithm diffuses labels in a balanced approach from both core and border nodes to expand communities. Next, a label selection step is performed to ensure that each node is surrendered by the most appropriate label. Finally, by utilizing a fast merge step, final communities are discovered. Besides, the proposed method not only has a fast convergence speed, but also provides stable and accurate results. Moreover, there is no randomness as well as adjustable parameter in the LBLD algorithm. Performed experiments on real-world and synthetic networks show the superiority of the LBLD method compared with examined algorithms.