Robust Anomaly Detection with Graph Neural Networks using Controllability

TL;DR

This paper introduces novel methods that incorporate average controllability into graph neural networks to improve anomaly detection performance, especially in sparse and imbalanced datasets.

Contribution

It proposes two innovative approaches to integrate controllability measures into graph models, enhancing anomaly detection capabilities.

Findings

Improved detection accuracy on real-world and synthetic networks.

Controllability-based features outperform traditional methods.

Enhanced robustness in sparse and imbalanced data scenarios.

Abstract

Anomaly detection in complex domains poses significant challenges due to the need for extensive labeled data and the inherently imbalanced nature of anomalous versus benign samples. Graph-based machine learning models have emerged as a promising solution that combines attribute and relational data to uncover intricate patterns. However, the scarcity of anomalous data exacerbates the challenge, which requires innovative strategies to enhance model learning with limited information. In this paper, we hypothesize that the incorporation of the influence of the nodes, quantified through average controllability, can significantly improve the performance of anomaly detection. We propose two novel approaches to integrate average controllability into graph-based frameworks: (1) using average controllability as an edge weight and (2) encoding it as a one-hot edge attribute vector. Through rigorous evaluation on real-world and synthetic networks with six state-of-the-art baselines, our proposed methods demonstrate improved performance in identifying anomalies, highlighting the critical role of controllability measures in enhancing the performance of graph machine learning models. This work underscores the potential of integrating average controllability as additional metrics to address the challenges of anomaly detection in sparse and imbalanced datasets.