通过对比学习重构图结构，提升属性网络异常检测精度
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction
- 构建双对比机制，利用原始与增强图的重构差异定位异常
- 在多个基准数据集上优于现有方法，显著提升异常识别率
- 适合处理含属性的复杂网络，尤其擅长发现隐蔽新类型异常
基于图的方法是识别欺诈、安全漏洞和系统故障等异常事件的有效手段。然而，以往工作多关注图结构复杂性与预定义异常，常忽视数据属性和新兴异常的影响。本文提出DCOR，一种融合重建与对比学习的属性网络异常检测新方法。该方法基于图神经网络框架，通过对比原始图与增强图的重构邻接矩阵和特征矩阵，有效捕捉细微异常。在多个基准数据集上开展全面实验，采用标准评估指标验证。结果表明，DCOR显著优于现有先进方法，在属性网络中展现出强大异常发现能力，具备揭示新型异常模式的潜力。
原文摘要 · Abstract (English)
Anomaly detection using a network-based approach is one of the most efficient ways to identify abnormal events such as fraud, security breaches, and system faults in a variety of applied domains. While most of the earlier works address the complex nature of graph-structured data and predefined anomalies, the impact of data attributes and emerging anomalies are often neglected. This paper introduces DCOR, a novel approach on attributed networks that integrates reconstruction-based anomaly detection with Contrastive Learning. Utilizing a Graph Neural Network (GNN) framework, DCOR contrasts the reconstructed adjacency and feature matrices from both the original and augmented graphs to detect subtle anomalies. We employed comprehensive experimental studies on benchmark datasets through standard evaluation measures. The results show that DCOR significantly outperforms state-of-the-art methods. Obtained results demonstrate the efficacy of proposed approach in attributed networks with the potential of uncovering new patterns of anomalies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。