摘要：We propose a statistical model for graphs with a core-periphery structure. We give a precise notion of what it means for a graph to have this structure, based on the sparsity properties of the subgraphs of core and periphery nodes. We present a class of sparse graphs with such properties, and provide methods to simulate from this class, and to perform posterior inference. We demonstrate that our model can detect core-periphery structure in simulated and real-world networks.
关键词：05C80; 60G55; 62F15; Bayesian nonparametrics; completely random measures; networks; Point processes; Poisson random measures; Random graphs; Sparsity
