Learning the Latent State Space of Time-Varying Graphs

TL;DR

This paper introduces a framework for modeling the dynamic structure of time-varying graphs, specifically applied to email communication data, to identify real-time events and changes in the graph's behavior.

Contribution

It presents a novel approach to learn the latent state space of time-varying graphs and compares discrete and probabilistic representations for capturing temporal relationships.

Findings

The framework successfully identifies subsequences corresponding to real-world events.

Probabilistic and discrete models offer different insights into graph dynamics.

The approach reveals non-stationary behavior in email communication patterns.

Abstract

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.