Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach

• URL: http://arxiv.org/abs/2308.01063v1
• Date: Wed, 2 Aug 2023 10:22:04 GMT
• Title: Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach
• Authors: Xing Ai, Jialong Zhou, Yulin Zhu, Gaolei Li, Tomasz P. Michalak, Xiapu Luo, Kai Zhou
• Abstract summary: This paper introduces a novel unsupervised framework for a new task called Group-level Graph Anomaly Detection (Gr-GAD) The proposed framework first employs a variant of Graph AutoEncoder (GAE) to locate anchor nodes that belong to potential anomaly groups by capturing long-range inconsistencies. The experimental results on both real-world and synthetic datasets demonstrate that the proposed framework shows superior performance in identifying and localizing anomaly groups.
• Score: 25.383587951822964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. However, existing graph anomaly detection algorithms focus on distinguishing individual entities (nodes or graphs) and overlook the possibility of anomalous groups within the graph. To address this limitation, this paper introduces a novel unsupervised framework for a new task called Group-level Graph Anomaly Detection (Gr-GAD). The proposed framework first employs a variant of Graph AutoEncoder (GAE) to locate anchor nodes that belong to potential anomaly groups by capturing long-range inconsistencies. Subsequently, group sampling is employed to sample candidate groups, which are then fed into the proposed Topology Pattern-based Graph Contrastive Learning (TPGCL) method. TPGCL utilizes the topology patterns of groups as clues to generate embeddings for each candidate group and thus distinct anomaly groups. The experimental results on both real-world and synthetic datasets demonstrate that the proposed framework shows superior performance in identifying and localizing anomaly groups, highlighting it as a promising solution for Gr-GAD. Datasets and codes of the proposed framework are at the github repository https://anonymous.4open.science/r/Topology-Pattern-Enhanced-Unsupervised-Group-level-Graph-Anomaly-D etection.

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