Core Periphery Structures in Weighted Graphs Using Greedy Growth

Abstract

Core periphery structure is a meso-scale property of complex networks. Core periphery structures can help identify the relationships between cohesive core clusters surrounded by sparse peripheries. The knowledge about such relationships can have many practical applications in real world complex networks. For example, in a web based network between all blogs on different topics, peripheries connecting popular groups could help in the study of flow of information across the web. In this paper, we propose a construction of core periphery structures for weighted graphs. We present a greedy growth based algorithm to extract core periphery structures in weighted graphs. We also score the core periphery associations as a measure of distance between them. Through extensive experimentation using two synthetic and two real world Protein-Protein Interaction (PPI) networks, we demonstrate the usefulness of core periphery structures over simple overlapping clusters obtained by a state of the art clustering algorithm called ClusterONE.