基于异常感知的变分图自编码器的图级异常检测算法
Anomaly-Aware Variational Graph Autoencoder Based Graph-Level Anomaly Detection Algorithm
林馥 1李明康 1罗学雄 2张书豪 1张越 3王梓桐3
作者信息
- 1. 武汉大学计算机学院 武汉 430072
- 2. 麦考瑞大学计算机学院 澳大利亚 悉尼 2122
- 3. 武汉大学网络安全学院 武汉 430072
- 折叠
摘要
图异常检测在识别复杂数据结构的异常模式中具有重要作用,被广泛地应用于有害分子识别、金融欺诈检测、社交网络分析等领域.但目前的图异常检测研究大多数聚焦在节点级别的异常检测,针对图级别的异常检测方法仍然较少,且这些方法并不能对异常图数据进行充分挖掘,且对异常标签比较敏感,无法有效地捕捉异常样本的特征,存在模型泛化能力差、性能翻转问题,异常检测能力有待提升.提出了一种基于异常感知的变分图自编码器的图级异常检测算法(anomaly-aware variational graph autoencoder based graph-level anomaly detection algorithm,VGAE-D),利用具有异常感知能力的变分图自编码器提取正常图和异常图数据的特征,并差异化正常图和异常图在编码空间中的编码信息分布,对图编码信息进一步挖掘来计算图的异常得分.在不同领域的 8个公开数据集上进行实验,实验结果表明,提出的图级别异常检测方法能有效地对不同数据集中的异常图进行识别,异常检测性能高于目前主流的图级别异常方法,且具有少异常样本学习能力,较大程度上克服了性能翻转问题.
Abstract
Graph anomaly detection plays a crucial role in identifying abnormal patterns within complex data structures,and finds applications in various domains such as malicious molecule identification,financial fraud detection,and social network analysis.While existing research in graph anomaly detection predominantly focuses on node-level anomaly detection,there is a scarcity of methods specifically designed for graph-level anomaly detection.Moreover,these methods often fail to adequately explore anomalous graph data,are sensitive to anomaly labels,struggle to capture features of anomalous samples effectively,exhibit poor model generalization,and suffer from performance reversal issues.There is a need for improvement in anomaly detection capabilities.we propose an anomaly-aware variational graph autoencoder based graph-level anomaly detection algorithm(VGAE-D),which utilizes an anomaly-aware variational graph autoencoder to simultaneously extract features from normal and anomalous graph data.This algorithm distinguishes the encoding information of normal and anomalous graphs in the encoding space,further exploring the graph encoding information to compute anomaly scores.Experimental evaluations on eight publicly available datasets from diverse domains demonstrate the effectiveness of the proposed graph-level anomaly detection algorithm.The results indicate that VGAE-D can efficiently identify anomalous graphs in different datasets,outperforming mainstream graph-level anomaly detection methods.Additionally,the algorithm exhibits a capability for learning from few anomalous samples,mitigating the issue of performance inversion to a large extent.
关键词
图级别异常检测/图神经网络/变分图自编码器/图表示学习/少样本学习Key words
graph-level anomaly detection/graph neural networks/variational graph autoencoder/graph representation learning/few-shot learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据