DCOR：基于双对比学习重建的属性网络异常检测
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction
AI总结：
DCOR提出一种结合重建与对比学习的属性网络异常检测方法，利用图神经网络对比原始与增强图的重建矩阵，在基准数据集上显著优于现有方法。
摘要：
基于网络的方法进行异常检测是在欺诈、安全漏洞和系统故障等多种应用领域中识别异常事件的最有效方式之一。尽管大多数早期工作解决了图结构数据的复杂性和预定义异常，但数据属性的影响和新出现的异常往往被忽视。本文介绍了DCOR，一种针对属性网络的新方法，它将基于重建的异常检测与对比学习相结合。利用图神经网络框架，DCOR对比原始图和增强图的重建邻接矩阵和特征矩阵，以检测细微的异常。我们通过标准评估指标在基准数据集上进行了全面的实验研究。结果表明，DCOR显著优于最先进的方法。所得结果证明了所提方法在属性网络中的有效性，并具有发现新异常模式的潜力。
英文摘要：
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.
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。
