Processing Evolving Social Networks for Change Detection Based on Centrality Measures

Abstract

Social networks have an evolving characteristic due to the continuous interaction between users, with nodes associating and disassociating with each other as time flies. The analysis of such networks is especially challenging, because it needs to be performed with an online approach, under the one-pass constraint of data streams. Such evolving behavior leads to changes in the network topology that can be investigated under different perspectives. In this work we focus on the analysis of nodes position evolution—a node-centric perspective. Our goal is to spot change-points in an evolving network at which a node deviates from its normal behavior. Therefore, we propose a change detection model for processing evolving network streams which employs three different aggregating mechanisms for tracking the evolution of centrality metrics of a node. Our model is space and time efficient with memory less mechanisms and in other mechanisms at most we require the network of current time step T only. Additionally, we also compare the influence on different centralities’ fluctuations by the dynamics of real-world preferences. Consecutively, we apply our model in the user preference change detection task, reaching competitive levels of accuracy on Twitter network.