Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning

• URL: http://arxiv.org/abs/2503.12037v1
• Date: Sat, 15 Mar 2025 08:08:13 GMT
• Title: Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning
• Authors: Hang Ni, Jindong Han, Nengjun Zhu, Hao Liu,
• Abstract summary: Graph Anomaly (GAD) plays a vital role in various data mining applications such as e-commerce fraud prevention and malicious user detection.<n>We propose a Heterophilic Graph Detection (HGE) module to learn distinguishable abnormal representations for potential anomalies.<n>Then, we propose a Multi-Hypersphere Learning (MHL) module to enhance the detection capability for context-dependent anomalies.
• Score: 7.277472116667557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Anomaly Detection (GAD) plays a vital role in various data mining applications such as e-commerce fraud prevention and malicious user detection. Recently, Graph Neural Network (GNN) based approach has demonstrated great effectiveness in GAD by first encoding graph data into low-dimensional representations and then identifying anomalies under the guidance of supervised or unsupervised signals. However, existing GNN-based approaches implicitly follow the homophily principle (i.e., the "like attracts like" phenomenon) and fail to learn discriminative embedding for anomalies that connect vast normal nodes. Moreover, such approaches identify anomalies in a unified global perspective but overlook diversified abnormal patterns conditioned on local graph context, leading to suboptimal performance. To overcome the aforementioned limitations, in this paper, we propose a Multi-hypersphere Heterophilic Graph Learning (MHetGL) framework for unsupervised GAD. Specifically, we first devise a Heterophilic Graph Encoding (HGE) module to learn distinguishable representations for potential anomalies by purifying and augmenting their neighborhood in a fully unsupervised manner. Then, we propose a Multi-Hypersphere Learning (MHL) module to enhance the detection capability for context-dependent anomalies by jointly incorporating critical patterns from both global and local perspectives. Extensive experiments on ten real-world datasets show that MHetGL outperforms 14 baselines. Our code is publicly available at https://github.com/KennyNH/MHetGL.

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