Cluster Aware Graph Anomaly Detection

TL;DR

This paper introduces CARE, a novel cluster-aware multi-view graph anomaly detection method that effectively captures node affinities and mitigates pseudo-label bias, outperforming existing methods on multiple datasets.

Contribution

We propose CARE, a new unsupervised multi-view graph anomaly detection approach that leverages pseudo-labels and a similarity-guided loss to improve detection accuracy without strong graph assumptions.

Findings

CARE outperforms competitors by over 39% on Amazon dataset (AUPRC)

CARE achieves 18.7% improvement on YelpChi dataset (AUROC)

The similarity-guided loss is a variant of contrastive learning that reduces pseudo-label bias.

Abstract

Graph anomaly detection has gained significant attention across various domains, particularly in critical applications like fraud detection in e-commerce platforms and insider threat detection in cybersecurity. Usually, these data are composed of multiple types (e.g., user information and transaction records for financial data), thus exhibiting view heterogeneity. However, in the era of big data, the heterogeneity of views and the lack of label information pose substantial challenges to traditional approaches. Existing unsupervised graph anomaly detection methods often struggle with high-dimensionality issues, rely on strong assumptions about graph structures or fail to handle complex multi-view graphs. To address these challenges, we propose a cluster aware multi-view graph anomaly detection method, called CARE. Our approach captures both local and global node affinities by augmenting the graph's adjacency matrix with the pseudo-label (i.e., soft membership assignments) without any strong assumption about the graph. To mitigate potential biases from the pseudo-label, we introduce a similarity-guided loss. Theoretically, we show that the proposed similarity-guided loss is a variant of contrastive learning loss, and we present how this loss alleviates the bias introduced by pseudo-label with the connection to graph spectral clustering. Experimental results on several datasets demonstrate the effectiveness and efficiency of our proposed framework. Specifically, CARE outperforms the second-best competitors by more than 39% on the Amazon dataset with respect to AUPRC and 18.7% on the YelpChi dataset with respect to AUROC. The code of our method is available at the GitHub link: https://github.com/zhenglecheng/CARE-demo.