Multi-Level Anomaly Detection on Time-Varying Graph Data

TL;DR

This paper introduces a multi-scale anomaly detection framework for time-varying graph data, enhancing interpretability and accuracy by modeling hierarchical community structures and enabling detailed visualization of anomalies.

Contribution

It generalizes the BTER model for flexible community structure and develops hierarchical anomaly detectors that outperform baseline methods on real and synthetic datasets.

Findings

Outperforms baseline Gaussian method in anomaly detection accuracy.

Effectively detects anomalies at node, subgraph, and graph levels.

Successfully applied to NCAA football data with high precision and recall.

Abstract

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.