A fast and robust kernel optimization method for core–periphery detection in directed and weighted graphs

Abstract

Abstract Many graph mining tasks can be viewed as classification problems on high dimensional data. Within this class we consider the issue of discovering core-periphery structure, which has wide applications in the economic and social sciences. In contrast to many current approaches, we allow for weighted and directed edges and we do not assume that the overall network is connected. Our approach extends recent work on a relevant relaxed nonlinear optimization problem. In the directed, weighted setting, we derive and analyze a globally convergent iterative algorithm. We also relate the algorithm to a maximum likelihood reordering problem on an appropriate core-periphery random graph model. We illustrate the effectiveness of the new algorithm on a large scale directed email network.