计算机科学与探索2024，Vol.18Issue(10)：2678-2689,12.DOI:10.3778/j.issn.1673-9418.2308043
高阶结构增强的跨视图无负样本对比的图异常检测算法
Cross-View Negative-Free Contrastive Learning for Graph Anomaly Detection with High-Order Structure Augmentation
摘要
Abstract
Graph anomaly detection has practical applications in various fields,such as cyber security,financial eval-uation and medical care.Recently,contrastive-based and generative-based detection frameworks have achieved re-markable performance improvements.However,most of the existing paradigms overlook the drawback that the GCN-based framework may unconsciously aggregate abnormal nodes with their neighborhood normal partners.Moreover,these detection algorithms lack attention to high-order structural information.These lead to a reduction in the distinction between normal nodes and their opponents.To bridge the gaps above,this paper proposes a cross-view negative-free contrastive learning utilizing high-order structure for graph anomaly detection(CNCL-GAD)in this paper.Especially,different from the existing single-view contrastive paradigm,this paper develops the high-order structure as the augmented view to introduce more global abnormality discrimination with multi-view contrastive learning for graph anomaly detection(GAD).Then,to mitigate the false-negative phenomenon of imbalanced data in GAD tasks where the majority of selected contrastive negative samples are normal subgraphs,this paper proposes the cross-view negative-free contrastive strategy to only pull the positive subgraphs'pairs between two views as close as possible.Furthermore,this paper integrates intra-view node-subgraph contrastive modules,attribute recon-struction modules,and cross-view subgraph-subgraph contrastive modules to simultaneously obtain more distinc-tions on structure and attribute.The extensive experiments conducted on benchmark datasets show that the proposed method achieves competitive or even superior performance compared with existing competitors.关键词
无负样本对比/图异常检测/高阶结构Key words
negative-free contrastive/graph anomaly detection/high-order structure分类
信息技术与安全科学引用本文复制引用
金虎,胡婧韬,王思为,祝恩,罗磊,段景灿..高阶结构增强的跨视图无负样本对比的图异常检测算法[J].计算机科学与探索,2024,18(10):2678-2689,12.基金项目
国家重点研发计划(2020AAA0107100) （2020AAA0107100）
国家自然科学基金(62276271,61872377).This work was supported by the National Key Research and Development Program of China(2020AAA0107100),and the National Nat-ural Science Foundation of China(62276271,61872377). （62276271,61872377）
