ExCoDE: a Tool for Discovering and Visualizing Regions of Correlation in Dynamic Networks Dynamic graphs are valuable means to represent the volatility of realworld 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 realworld 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.

Evolution of communities in dynamic social networks: An efficient mapbased approach Abstract   The expanded domain of expert system applications has risen the impact of modeling and analysis of community evolution in social networks as an important part of the decisionmaking process. Social networks are timevariant systems, evolving through entities joining or leaving networks and establishing or terminating relationships. In this article, we study evolution of social networks at the level of community structure, by tracking different transformations of communities over time. Upon experimentation, we observed that a considerable portion of community evolution is partial events such as partial merge. Therefore, we define a broader set of community evolution to include partial events. Furthermore, we introduce ICEM, a novel method for Identification of Community Evolution by Mapping. ICEM determines community evolution by tracking community members, implemented with a hashmap. ICEM maps each member to a (t, xe2x80x85c) pair, specifying it is last observed in time window t and community c. We evaluated our proposed approach with seventeen publicly available social network datasets and compared its performance against other wellknown methods in the literature. Our experimental results indicated the performance superiority of our proposed solution. Additionally, we conducted separate comprehensive experiments using three community detection algorithms to highlight the effect of choosing different community discovery methods on community evolution results.