Learning the Latent State Space of Time-Varying Graphs
This work addresses the challenge of handling non-stationary characteristics in time-varying graphs for applications like social networks and communication tools, but it appears incremental as it builds on existing graph modeling approaches.
The authors tackled the problem of modeling dynamic structure in time-varying graphs, specifically an email graph, by developing a framework to learn its latent state space and identify subsequences corresponding to global real-time events like vacations and breaks.
From social networks to Internet applications, a wide variety of electronic communication tools are producing streams of graph data; where the nodes represent users and the edges represent the contacts between them over time. This has led to an increased interest in mechanisms to model the dynamic structure of time-varying graphs. In this work, we develop a framework for learning the latent state space of a time-varying email graph. We show how the framework can be used to find subsequences that correspond to global real-time events in the Email graph (e.g. vacations, breaks, ...etc.). These events impact the underlying graph process to make its characteristics non-stationary. Within the framework, we compare two different representations of the temporal relationships; discrete vs. probabilistic. We use the two representations as inputs to a mixture model to learn the latent state transitions that correspond to important changes in the Email graph structure over time.