An improved algorithm for generalized community structure inference in complex networks

In recent years, the research of the community detection is not only on the structure that densely connected internally, but also on the structure of more patterns, such as heterogeneity, overlapping, core-periphery. In this paper, we build the network model based on the random graph models and propose an improved algorithm to infer the generalized community structures. We achieve it by introducing the generalized Bernstein polynomials and computing the latent parameters of vertices. The algorithm is tested both on the computer-generated benchmark networks and the real-world networks. Results show that the algorithm makes better performances on convergence speed and is able to discover the latent continuous structures in networks.