ExCoDE: A tool for discovering and visualizing regions of correlation in dynamic networks

Abstract

Dynamic graphs are valuable means to represent the volatility of real-world networks. In such scenarios, dense subgraph mining is a widely studied task, as it can give insights about how the relationships change over time. However, there are cases in which these changes are correlated. For example, in a road network, a traffic accident affects also the traffic in the adjacent road segments. We consider the problem of detecting dense regions of correlation in dynamically evolving networks, and demonstrate EXCODE, a system that solves two variants of the problem, which are based on two different density measures. It enumerates all the subgraphs satisfying certain density and correlation constraints, but can also detect compact subsets of limited overlap. In this demonstration, the audience can try this tool with real-world datasets, hence visualizing, interacting, and exploring the dense correlated subgraphs discovered in the mining process. An interactive panel allows them to learn where the correlations are located in the network, how the regions of correlation are related to each other, and how they evolve over time.