Multitask Active Learning for Graph Anomaly Detection

TL;DR

This paper introduces MITIGATE, a multitask active learning framework that improves graph anomaly detection by leveraging node classification tasks, confidence-based sampling, and masked aggregation to select informative nodes, outperforming existing methods.

Contribution

MITIGATE is a novel multitask active learning approach that enhances graph anomaly detection by integrating node classification, confidence-based sampling, and masked aggregation mechanisms.

Findings

MITIGATE significantly outperforms state-of-the-art anomaly detection methods.

The framework effectively detects out-of-distribution nodes without known anomalies.

Empirical results on four datasets validate the approach's superiority.

Abstract

In the web era, graph machine learning has been widely used on ubiquitous graph-structured data. As a pivotal component for bolstering web security and enhancing the robustness of graph-based applications, the significance of graph anomaly detection is continually increasing. While Graph Neural Networks (GNNs) have demonstrated efficacy in supervised and semi-supervised graph anomaly detection, their performance is contingent upon the availability of sufficient ground truth labels. The labor-intensive nature of identifying anomalies from complex graph structures poses a significant challenge in real-world applications. Despite that, the indirect supervision signals from other tasks (e.g., node classification) are relatively abundant. In this paper, we propose a novel MultItask acTIve Graph Anomaly deTEction framework, namely MITIGATE. Firstly, by coupling node classification tasks, MITIGATE obtains the capability to detect out-of-distribution nodes without known anomalies. Secondly, MITIGATE quantifies the informativeness of nodes by the confidence difference across tasks, allowing samples with conflicting predictions to provide informative yet not excessively challenging information for subsequent training. Finally, to enhance the likelihood of selecting representative nodes that are distant from known patterns, MITIGATE adopts a masked aggregation mechanism for distance measurement, considering both inherent features of nodes and current labeled status. Empirical studies on four datasets demonstrate that MITIGATE significantly outperforms the state-of-the-art methods for anomaly detection. Our code is publicly available at: https://github.com/AhaChang/MITIGATE.