arXiv 2308.010632023-08-02cs.LGcs.AI

群组级图异常检测：一种拓扑模式增强的无监督方法

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

The Hong Kong Polytechnic University（香港理工大学）；Shanghai Jiao Tong University（上海交通大学）；University of Warsaw（华沙大学）

Xing Ai, Jialong Zhou, Yulin Zhu, Gaolei Li, Tomasz P. Michalak, Xiapu Luo, Kai Zhou

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AI总结：

针对现有图异常检测忽略异常群组的问题，提出一种拓扑模式增强的无监督框架Gr-GAD，通过GAE变体定位锚节点并结合TPGCL方法生成群组嵌入，有效识别和定位异常群组。

摘要：

图异常检测（GAD）已取得成功并广泛应用于欺诈检测、网络安全、金融安全和生物化学等多个领域。然而，现有的图异常检测算法侧重于区分单个实体（节点或图），忽略了图中异常群组的可能性。为了解决这一局限性，本文针对一种名为群组级图异常检测（Gr-GAD）的新任务，提出了一种新颖的无监督框架。该框架首先采用图自编码器（GAE）的一种变体，通过捕获长程不一致性来定位属于潜在异常群组的锚节点。随后，采用群组采样来采样候选群组，并将其输入到所提出的基于拓扑模式的图对比学习（TPGCL）方法中。TPGCL利用群组的拓扑模式作为线索，为每个候选群组生成嵌入，从而区分不同的异常群组。在真实世界和合成数据集上的实验结果表明，所提出的框架在识别和定位异常群组方面表现出优越的性能，突显其作为Gr-GAD的有前途的解决方案。该框架的数据集和代码位于github仓库https://anonymous.4open.science/r/Topology-Pattern-Enhanced-Unsupervised-Group-level-Graph-Anomaly-Detection。

英文摘要：

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-Detection.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。