Anomaly Detection in Time Series of Graphs using Fusion of Graph Invariants

Given a time series of graphs G(t)=(V,E(t)) , t=1,2,... , where the fixed vertex set V represents “actors” and an edge between vertex u and vertex v at time t(uv ∈ E(t)) represents the existence of a communications event between actors u and v during the t^th time period, we wish to detect anomalies and/or change points. We consider a collection of graph features, or invariants, and demonstrate that adaptive fusion provides superior inferential efficacy compared to naive equal weighting for a certain class of anomaly detection problems. Simulation results using a latent process model for time series of graphs, as well as illustrative experimental results for a time series of graphs derived from the Enron email data, show that a fusion statistic can provide superior inference compared to individual invariants alone. These results also demonstrate that an adaptive weighting scheme for fusion of invariants performs better than naive equal weighting.