ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness

TL;DR

ARISE introduces a novel substructure-aware framework for detecting both topology and attribute anomalies in attributed networks, leveraging dense substructure discovery and contrastive learning to improve detection accuracy.

Contribution

The paper proposes a new graph anomaly detection method that focuses on substructure recognition and contrastive learning, enhancing detection of collective and attribute anomalies.

Findings

Improves detection performance with up to 7.30% AUC gain.

Achieves up to 17.46% AUPRC improvement.

Effectively detects both topology and attribute anomalies.

Abstract

Recently, graph anomaly detection on attributed networks has attracted growing attention in data mining and machine learning communities. Apart from attribute anomalies, graph anomaly detection also aims at suspicious topological-abnormal nodes that exhibit collective anomalous behavior. Closely connected uncorrelated node groups form uncommonly dense substructures in the network. However, existing methods overlook that the topology anomaly detection performance can be improved by recognizing such a collective pattern. To this end, we propose a new graph anomaly detection framework on attributed networks via substructure awareness (ARISE for abbreviation). Unlike previous algorithms, we focus on the substructures in the graph to discern abnormalities. Specifically, we establish a region proposal module to discover high-density substructures in the network as suspicious regions. The average node-pair similarity can be regarded as the topology anomaly degree of nodes within substructures. Generally, the lower the similarity, the higher the probability that internal nodes are topology anomalies. To distill better embeddings of node attributes, we further introduce a graph contrastive learning scheme, which observes attribute anomalies in the meantime. In this way, ARISE can detect both topology and attribute anomalies. Ultimately, extensive experiments on benchmark datasets show that ARISE greatly improves detection performance (up to 7.30% AUC and 17.46% AUPRC gains) compared to state-of-the-art attributed networks anomaly detection (ANAD) algorithms.