A0260
Title: UBSea: A unified community detection framework
Authors: Xiancheng Lin - University of California at Davis (United States)
Hao Chen - University of California at Davis (United States) [presenting]
Abstract: Detecting communities in networks and graphs is an important task across many disciplines, such as statistics, social science and engineering. There are generally three different kinds of mixing patterns for the case of two communities: assortative mixing, disassortative mixing and core-periphery structure. Modularity optimization is a classical way of fitting network models with communities. However, it can only deal with assortative mixing and disassortative mixing when the mixing pattern is known, and the core-periphery structure is not discovered. Modularity in a strategic way is extended, and a new framework is proposed based on unified bigroups standardized edge-count analysis (UBSea). It can address all the formerly mentioned community mixing structures. In addition, this new framework is able to automatically choose the mixing type to fit the networks. Simulation studies show that the new framework has superb performance in a wide range of settings under the stochastic block model and the degree-corrected stochastic block model. It is shown that the new approach produces a consistent estimate of the communities under a suitable signal-to-noise-ratio condition for the case of a block model with two communities for both undirected and directed networks. The new method is illustrated through applications to several real-world datasets.
