基于数据增强的多视图对比学习图异常检测

李一凡; 李家印; 林兴澎; 戴远飞; 许力

doi:10.11959/j.issn.2096-109x.2024075

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基于数据增强的多视图对比学习图异常检测

学术论文 | 更新时间：2024-12-20

- 基于数据增强的多视图对比学习图异常检测
- Data augmentation based multi-view contrastive learning graph anomaly detection
- 网络与信息安全学报 2024年10卷第5期页码：163-174
- 作者机构：
  
  1.福建师范大学计算机与网络空间安全学院，福建福州 350000
  2.福建省网络安全与密码技术重点实验室，福建福州 350000
  3.南京工业大学计算机与信息工程学院，江苏南京 210000
- 作者简介：
  
  [ "李一凡（2000- ），男，湖南邵阳人，福建师范大学硕士生，主要研究方向为异常检测、图神经网络。" ]
  [ "李家印（1990- ），男，山东济宁人，博士，福建师范大学讲师，主要研究方向为安全联邦学习、人工智能安全。" ]
  [ "林兴澎（2001- ），男，福建三平人，福建师范大学硕士生，主要研究方向为谣言检测、图神经网络。" ]
  [ "戴远飞（1992- ），男，山东荣成人，博士，南京工业大学讲师，主要研究方向为自然语言处理、知识图谱。" ]
  [ "许力（1970- ），男，福建福州人，博士，福建师范大学教授、博士生导师，主要研究方向为网络与信息安全、复杂系统建模与分析。" ]
- 基金信息：
  
  国家自然科学基金(62471139;U1905211;62202221);国家科技项目备案-中央引导地方科技发展专项(2023L3007);江苏省自然科学基金青年项目(BK20220331);福建省自然科学基金(2023J05128);福建省科技创新重点项目(2022G02003)
- DOI：10.11959/j.issn.2096-109x.2024075
  中图分类号： TP393
- 收稿：2024-04-22，
  
  修回：2024-07-19，
  
  纸质出版：2024-10-25
- 稿件说明：
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李一凡, 李家印, 林兴澎, 等. 基于数据增强的多视图对比学习图异常检测[J]. 网络与信息安全学报, 2024, 10(5): 163-174.

Format: LI Y F, LI J Y, LIN X P, et al. Data augmentation based multi-view contrastive learning graph anomaly detection[J]. Chinese Journal of Network and Information Security, 2024, 10(5): 163-174.
李一凡, 李家印, 林兴澎, 等. 基于数据增强的多视图对比学习图异常检测[J]. 网络与信息安全学报, 2024, 10(5): 163-174. DOI： 10.11959/j.issn.2096-109x.2024075.

Format: LI Y F, LI J Y, LIN X P, et al. Data augmentation based multi-view contrastive learning graph anomaly detection[J]. Chinese Journal of Network and Information Security, 2024, 10(5): 163-174. DOI： 10.11959/j.issn.2096-109x.2024075.

摘要

图异常检测在预防金融欺诈、网络入侵等有害事件上具有重要价值。基于对比的异常检测方法尽管能够根据异常节点实例对的不一致性有效挖掘异常信息，避免因采用自编码架构导致模型需要全图训练的弊端，但是现有的大多数基于对比的图异常检测方法仅关注节点-子图的对比模式，忽略了采样的节点-子图实例对中只包含目标节点的局部信息，同时没有考虑各子图对目标节点的重要性，导致节点全局信息缺失和对比模式过于泛化的问题。为解决上述问题，提升图异常检测的准确度，提出了一种基于数据增强的多视图对比学习图异常检测（data augmentation based multi-view contrastive learning graph anomaly detection，DAMC-GAD）方法。具体地，提出了一种用于异常检测的图数据增强方法，通过目标节点的相对局部结构和自身属性关联距离较远的节点，以构建全局信息丰富的增强视图。引入逐层采样结合节点-子图对比，根据子图重要性制定对比模型的优化策略。通过互补的融合策略构建基于数据增强的多视图对比学习模型，在合成异常与真实异常数据集上进行大量实验，实验结果表明，DAMC-GAD在两类数据集上均优于目前先进的基线模型。

Abstract

Graph anomaly detection is valuable in preventing harmful events such as financial fraud and network intrusion. Although contrast-based anomaly detection methods could effectively mine anomaly information based on the inconsistency of anomalous node instance pairs

avoiding the drawback of using self-coding architecture that led to the need for full graph training for the model. However

most existing contrast-based graph anomaly detection methods focused only on node-subgraph contrast patterns

ignoring the fact that the sampled node-subgraph instance pairs contained only the local information of the target node

and at the same time did not take into account the importance of each subgraph to the target node

which led to the lack of global information about the node and the emergence of the problem that the contrast patterns were too generalized. In order to solve the problems mentioned above and to improve the accuracy of graph anomaly detection

a graph anomaly detection by data augmentation and multi-view contrastive learning (DAMC-GAD) was proposed. Specifically

a graph data augmentation method for anomaly detection was proposed

in which the relative local structure of target nodes and their own attributes were used to correlate distant nodes in order to construct an augmented view that was rich in global information. Layer-by-layer sampling combined with node-subgraph contrast was introduced

and optimization strategies for the contrast model were developed based on the importance of the subgraph. A multi-view contrastive learning model with data augmentation was constructed through a complementary fusion strategy

and extensive experiments were conducted on synthetic anomaly and real anomaly datasets

which show that DAMC-GAD outperforms the current state-of-the-art baseline model on both types of datasets.

关键词

Keywords

references

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