Community Detection for Multilayer Heterogeneous Network
Fan Yang, Fengshuo Zhang
备注：
We were not aware of the fact that similar ideas have already appeared in the literature. We decide to withdraw the manuscript and apologize for any confusion
Many real world networks consist of multiple types of nodes with edges that are heterogeneous in nature. However, most of the existing work for community detection only focused on homogeneous network consisting of a single layer. In this paper, we propose a modified Degree-Corrected Stochastic Model (DCBM) for modeling multilayer heterogeneous network. We develop a spectral clustering method that can unify the information contained in each sub-network, and demonstrate its efficiency to detect communities on simulated data and on Authorship/Citation network data. As a by-product, we present a novel algorithm called BiScore for clustering bipartite network under DCBM, and show that under mild conditions BiScore is guaranteed to yield consistent results.
A Multilayer Probit Network Model for Community Detection with Dependent Edges and Layers
The Chinese University of Hong Kong（香港中文大学）；Southern University of Science and Technology（南方科技大学）；Shanghai Center for Mathematical Sciences, Fudan University（复旦大学上海数学中心）
Online Learning for Autoregressive Multilayer Stochastic Block Models under Stationarity and Non-Stationarity
School of Mathematics and Statistics, University of Melbourne（墨尔本大学数学与统计学院）；Department of Mathematics and Statistics, Auburn University（奥本大学数学与统计系）；Department of Statistics, University of Warwick（华威大学统计系）
