基于超球面对偶学习的双通道图异常检测方法

李青; 钟将; 倪航

doi:10.12263/DZXB.20231106

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基于超球面对偶学习的双通道图异常检测方法

学术论文 | 更新时间：2025-12-24

- 基于超球面对偶学习的双通道图异常检测方法
- Anomaly Detection with Dual-Channel Heterogeneous Graph Neural Network Based on Hypersphere Dual Learning
- 电子学报 2024年52卷第7期页码：2212-2218
- 作者机构：
  
  1.西北工业大学计算机学院，陕西西安 710072
  2.重庆大学计算机学院，重庆 400044
- 作者简介：
  
  [ "李青女，1989年5月出生于陕西省西安市.现为西北工业大学计算机学院副教授、硕士生导师，主要研究方向为图数据挖掘与自然语言处理.在国内外发表学术论文40余篇.E-mail: qingli@nwpu.edu.cn" ]
  [ "钟将男，1974年4月出生于河南省南阳市.1995年毕业于重庆大学计算机学院，现为重庆大学教授，从事大数据分析与挖掘方面的研究工作.E-mail: zhongjiang@cqu.edu.cn" ]
  [ "倪航男，2001年11月出生于江苏省南通市.2023年毕业于西北工业大学教育实验学院.主要研究方向为深度学习.E-mail: nihang@mail.nwpu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62102316;62171382);中央高校基本科研业务费资助项目（No.‍G2021KY05114）;“十四五”共用信息系统装备预先研究项目(315197202);航空科学基金(20200051053002)
- DOI：10.12263/DZXB.20231106
  中图分类号： TP311.1
- 收稿：2023-11-28，
  
  修回：2024-05-23，
  
  纸质出版：2024-07-25
- 稿件说明：
移动端阅览
李青, 钟将, 倪航. 基于超球面对偶学习的双通道图异常检测方法[J]. 电子学报, 2024, 52(07): 2212-2218.

LI Qing, ZHONG Jiang, NI Hang. Anomaly Detection with Dual-Channel Heterogeneous Graph Neural Network Based on Hypersphere Dual Learning[J]. Acta Electronica Sinica, 2024, 52(07): 2212-2218.
李青, 钟将, 倪航. 基于超球面对偶学习的双通道图异常检测方法[J]. 电子学报, 2024, 52(07): 2212-2218. DOI：10.12263/DZXB.20231106

LI Qing, ZHONG Jiang, NI Hang. Anomaly Detection with Dual-Channel Heterogeneous Graph Neural Network Based on Hypersphere Dual Learning[J]. Acta Electronica Sinica, 2024, 52(07): 2212-2218. DOI：10.12263/DZXB.20231106

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.

Abstract

Graph anomaly detection

as a crucial data mining task

focuses on identifying anomalous nodes that significantly deviate from the majority of the nodes. With the advancement of unsupervised graph neural network techniques

various efficient methods have been developed to detect potential anomalies in graph data

including those based on density estimation and generative adversarial networks. However

these methods often focus on generating high-quality representations 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 introduces 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 similarity 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 exceeded 0.9 in most cases

achieving industry-leading levels and further proving the HD-GAD model's efficiency and performance advantages in graph anomaly detection tasks.

关键词

Keywords

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