Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective

TL;DR

This paper introduces AD-GCL, a graph contrastive learning framework designed to improve anomaly detection robustness in networks with structural imbalance, especially for low-degree tail nodes.

Contribution

The paper proposes a novel GCL-based method with neighbor pruning and neighbor completion strategies to enhance tail anomaly detection in imbalanced graphs.

Findings

AD-GCL outperforms existing methods in detecting head and tail anomalies.

The framework improves robustness against structural imbalance.

Experimental results validate the effectiveness of the proposed strategies.

Abstract

The superiority of graph contrastive learning (GCL) has prompted its application to anomaly detection tasks for more powerful risk warning systems. Unfortunately, existing GCL-based models tend to excessively prioritize overall detection performance while neglecting robustness to structural imbalance, which can be problematic for many real-world networks following power-law degree distributions. Particularly, GCL-based methods may fail to capture tail anomalies (abnormal nodes with low degrees). This raises concerns about the security and robustness of current anomaly detection algorithms and therefore hinders their applicability in a variety of realistic high-risk scenarios. To the best of our knowledge, research on the robustness of graph anomaly detection to structural imbalance has received little scrutiny. To address the above issues, this paper presents a novel GCL-based framework named AD-GCL. It devises the neighbor pruning strategy to filter noisy edges for head nodes and facilitate the detection of genuine tail nodes by aligning from head nodes to forged tail nodes. Moreover, AD-GCL actively explores potential neighbors to enlarge the receptive field of tail nodes through anomaly-guided neighbor completion. We further introduce intra- and inter-view consistency loss of the original and augmentation graph for enhanced representation. The performance evaluation of the whole, head, and tail nodes on multiple datasets validates the comprehensive superiority of the proposed AD-GCL in detecting both head anomalies and tail anomalies.