基于超球面对偶学习的双通道图异常检测方法

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万方数据
维普

中文摘要：图异常检测作为一项重要的数据挖掘任务,专注于识别与大多数节点显著偏离的异常节点.随着无监督图神经网络技术的进步,现已开发出了基于密度估计、对抗生成网络等多种高效识别图数据中潜在异常的方法.然而,这些方法更注重无监督图异常检测生成高质量的表征,而往往忽略了图异常的特性.因此,本文提出了一个双通道异构图异常检测模型(Dual-channel Heterogeneous Graph Anomaly Detection,HD-GAD).其模型基础架构包括双通道的图神经网络:全局子结构感知的图神经网络和局部子结构感知的图神经网络,用于图异常检测捕获全局和局部子结构属性.同时,基于对偶推断引入了多超球体学习目标(Multi-Hypersphere Learning,MHL),从宏观和介观超球体角度,分别测量在整个图/社区结构中偏离的异常节点.HD-GAD模型利用相似度函数EmbSim优化训练目标,以缓解多超球面学习中的模型坍问题.最后,在五种不同的数据集上进行了全面的实验.其AUC(Area Under Curve)值在大多数情况下均超过了0.9,达到了行业领先水平,进一步证明了HD-GAD模型在图异常检测任务上的高效性与性能优势.

外文标题：Anomaly Detection with Dual-Channel Heterogeneous Graph Neural Network Based on Hypersphere Dual Learning

外文摘要：Graph anomaly detection,as a crucial data mining task,focuses on identifying anomalous nodes that signif-icantly deviate from the majority of the nodes. With the advancement of unsupervised graph neural network techniques,var-ious efficient methods have been developed to detect potential anomalies in graph data,including those based on density es-timation and generative adversarial networks. However,these methods often focus on generating high-quality representa-tions for unsupervised graph anomaly detection and tend to overlook the characteristics of graph anomalies. Consequently,this paper proposes a dual-channel heterogeneous graph anomaly detection model (HD-GAD). Its architecture includes two graph neural networks,i.e. a global substructure-aware GNN (Graph Neural Network) and a local substructure-aware GNN,designed to capture global and local substructural properties for graph anomaly detection. Additionally,the model introduc-es a multi-hypersphere learning (MHL) objective based on dual inference,which measures anomalies deviating from the overall graph/community structure from macro and meso hypersphere perspectives. The HD-GAD model utilizes the simi-larity function EmbSim to optimize the training objective,mitigating model collapse issues in multi-hypersphere learning. Comprehensive experiments conducted on five different datasets demonstrated that the AUC (Area Under Curve) values ex-ceeded 0.9 in most cases,achieving industry-leading levels and further proving the HD-GAD model's efficiency and perfor-mance advantages in graph anomaly detection tasks.

外文关键词：

graph anomaly detectiongraph neural networkhypersphere learningdual-channel graph neural networkunsupervised learningdual learning

作者：

李青、钟将、倪航

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作者单位：

西北工业大学计算机学院,陕西西安 710072

重庆大学计算机学院,重庆 400044

关键词：

图异常检测图神经网络超球面学习双通道图神经网络无监督学习对偶学习

基金：

国家自然科学基金国家自然科学基金中央高校基本科研业务费资助项目"十四五"共用信息系统装备预先研究项目航空科学基金

项目编号：

6210231662171382G2021KY0511431519720220200051053002

出版年：

2024

DOI：

10.12263/DZXB.20231106

电子学报

中国电子学会

电子学报

CSTPCD北大核心

影响因子：1.237

ISSN：0372-2112

年,卷(期)：2024.52(7)