Information diffusion-aware likelihood maximization optimization for community detection

As a hot research topic in network science, community detection has attracted much attention of scholars. In recent years, many methods have emerged to discover the underlying community structure in the network. However, most of these methods need to take the network topology information as prior knowledge that is not feasible in practical cases. When information diffusion occurs in the network, one can observe the cascade data in which nodes participate in the propagation process, which reflects the network’s community structure to some extent. In this paper, we build a likelihood maximization model by utilizing the diffusion information and propose two different optimization algorithms to obtain community division of the network. Extensive experiments on various datasets show that our proposed methods achieve significant improvements in terms of accuracy, scalability, and efficiency of community detection compared with the existing state-of-the-art methods.