Extracting Structural Changes of Dynamic Networks based on the PageRank

Abstract

In this paper, we attempt to detect change points of a dynamic network structure. We focus on the nodes functions in a network and define the nodes function as the convergence curve of the PageRank score. For each node, we calculate the correlation coeffcients between the convergence curves in adjacent two snapshots of a time-varying network. Then, we propose the average of correlation coeffcients of all nodes as a measure of the change point of a network strucuture and refer to this measure as average similarity. Especially, when the average similarity shows the lower value, we assume that the network structure changes significantly. In our experiments using synthetic and real networks with artificial changes, we evaluate the eectiveness of our proposed measure.