A New Method on Modularity Gain Derivation and Enhanced LM

Abstract

Louvain Method (LM), one of mainstream community detection approach based on modularity optimization, is widely used by virtue of its nearly linear time complexity and high quality community detection but has some deficiencies with respect to the theory and efficiency. We firstly present a method to calculate the Q-gain after node leaving their community and improve the theoretical research in this field, considering there is no method to calculate the gain in the existing research. Secondly, in view of the high storage space demands of LM and the sparse nature of complex networks, we propose an isolated node separation strategy, which only remains the connected nodes in each iteration. The experimental results based on the synthetic and real networks illustrate the effectiveness and efficiency provided by our approach.