Optimal Timelines for Network Processes Structural models for network dynamics typically assume a discrete timeline of network events (node activation or link creation) and a stochastic generative process giving rise to new events based on the event history and the network structure. In order to employ these models for prediction, observational data is often aggregated at a fixed temporal resolution (e.g., minutes or days). However, the underlying network processes may xe2x80x9cspeed upxe2x80x9d or xe2x80x9cslow downxe2x80x9d at different points in time, rendering observations unlikely and predictions incorrect. The challenge is to optimize the timescale for the analysis of network event data, which in turn is based on structural models of the underlying network processes. We introduce the general problem of inferring the optimal temporal resolution for network event data. The goal is to map observed network events to discrete time steps by aggregation andor disaggregation of their original timeline such that they are collectively wellexplained by structural dynamics models. We unify network growth and information diffusion models and differentiate between short and longmemory processes. We demonstrate that while optimal temporal aggregation can be performed in polynomial time, disaggregationxe2x80x94and thus, the general timescale inference problemxe2x80x94is NPhard. We propose scalable heuristics for the problem, some with approximation guarantees, and employ them for missing event recovery and temporal link prediction, demonstrating significant improvements (absolute increase of 10% in F1 measure for event recovery and of 5% in AUC for link prediction) compared to employing the same algorithms on the default timescale of data collection.

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.