Context Correlation Discrepancy Analysis for Graph Anomaly Detection

作     者：Wang, Ruidong Xi, Liang Zhang, Fengbin Fan, Haoyi Yu, Xu Liu, Lei Yu, Shui Leung, Victor C. M. 

作者机构：Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321000 Peoples R China Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150080 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China China Univ Petr East China Qingdao Inst Software Qingdao 266580 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China Qilu Univ Technol Shandong Acad Sci Key Lab Comp Power Network & Informat Secur Minist Educ Jinan 250014 Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW 2007 Australia Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada 

出 版 物：《IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING》 (IEEE Trans Knowl Data Eng)

年 卷 期：2025年第37卷第1期

页      面：174-187页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Natural Science Foundation of Heilongjiang Province [LH2022F034] Chunhui Project Foundation of the Education Department of China [HZKY20220291] National Natural Science Foundation of China [62172249, 62472441] Innovation Capability Support Program of Shaanxi [2024RS-CXTD-01] 

主　　题：Anomaly detection Correlation Feature extraction Computer science Vectors Transformers Software Semantics Reviews Representation learning Graph anomaly detection graph neural networks canonical correlation analysis graph embedding 

摘      要：In unsupervised graph anomaly detection, existing methods usually focus on detecting outliers by learning local context information of nodes, while often ignoring the importance of global context. However, global context information can provide more comprehensive relationship information between nodes in the network. By considering the structure of the entire network, detection methods are able to identify potential dependencies and interaction patterns between nodes, which is crucial for anomaly detection. Therefore, we propose an innovative graph anomaly detection framework, termed CoCo (Context Correlation Discrepancy Analysis), which detects anomalies by meticulously evaluating variances in correlations. Specifically, CoCo leverages the strengths of Transformers in sequence processing to effectively capture both global and local contextual features of nodes by aggregating neighbor features at various hops. Subsequently, a correlation analysis module is employed to maximize the correlation between local and global contexts of each normal node. Unseen anomalies are ultimately detected by measuring the discrepancy in the correlation of nodes contextual features. Extensive experiments conducted on six datasets with synthetic outliers and five datasets with organic outliers have demonstrated the significant effectiveness of CoCo compared to existing methods.