Concept Drift and Anomaly Detection in Graph Streams
This addresses the challenge of monitoring dynamic graph data in domains like biology and image analysis, though it is incremental as it adapts existing change detection techniques to graphs.
The paper tackles the problem of detecting concept drift and anomalies in streams of attributed graphs with variable structure, by embedding graphs into vectors and applying change detection methods. It demonstrates effectiveness on biological molecules and drawings, showing improved performance over baselines.
Graph representations offer powerful and intuitive ways to describe data in a multitude of application domains. Here, we consider stochastic processes generating graphs and propose a methodology for detecting changes in stationarity of such processes. The methodology is general and considers a process generating attributed graphs with a variable number of vertices/edges, without the need to assume one-to-one correspondence between vertices at different time steps. The methodology acts by embedding every graph of the stream into a vector domain, where a conventional multivariate change detection procedure can be easily applied. We ground the soundness of our proposal by proving several theoretical results. In addition, we provide a specific implementation of the methodology and evaluate its effectiveness on several detection problems involving attributed graphs representing biological molecules and drawings. Experimental results are contrasted with respect to suitable baseline methods, demonstrating the effectiveness of our approach.