{"title":"Cross-Domain Graph Anomaly Detection via Graph Transfer and Graph Decouple","authors":"Changqin Huang, Xinxing Shi, Chengling Gao, Qintai Hu, Xiaodi Huang, Qionghao Huang, Ali Anaissi","doi":"10.1049/cit2.70014","DOIUrl":null,"url":null,"abstract":"<p>Cross-domain graph anomaly detection (CD-GAD) is a promising task that leverages knowledge from a labelled source graph to guide anomaly detection on an unlabelled target graph. CD-GAD classifies anomalies as unique or common based on their presence in both the source and target graphs. However, existing models often fail to fully explore domain-unique knowledge of the target graph for detecting unique anomalies. Additionally, they tend to focus solely on node-level differences, overlooking structural-level differences that provide complementary information for common anomaly detection. To address these issues, we propose a novel method, Synthetic Graph Anomaly Detection via Graph Transfer and Graph Decouple (GTGD), which effectively detects common and unique anomalies in the target graph. Specifically, our approach ensures deeper learning of domain-unique knowledge by decoupling the reconstruction graphs of common and unique features. Moreover, we simultaneously consider node-level and structural-level differences by transferring node and edge information from the source graph to the target graph, enabling comprehensive domain-common knowledge representation. Anomalies are detected using both common and unique features, with their synthetic score serving as the final result. Extensive experiments demonstrate the effectiveness of our approach, improving an average performance by 12.6<span></span><math>\n <semantics>\n <mrow>\n <mi>%</mi>\n </mrow>\n <annotation> $\\%$</annotation>\n </semantics></math> on the AUC-PR compared to state-of-the-art methods.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1089-1103"},"PeriodicalIF":7.3000,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70014","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CAAI Transactions on Intelligence Technology","FirstCategoryId":"94","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cit2.70014","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Cross-domain graph anomaly detection (CD-GAD) is a promising task that leverages knowledge from a labelled source graph to guide anomaly detection on an unlabelled target graph. CD-GAD classifies anomalies as unique or common based on their presence in both the source and target graphs. However, existing models often fail to fully explore domain-unique knowledge of the target graph for detecting unique anomalies. Additionally, they tend to focus solely on node-level differences, overlooking structural-level differences that provide complementary information for common anomaly detection. To address these issues, we propose a novel method, Synthetic Graph Anomaly Detection via Graph Transfer and Graph Decouple (GTGD), which effectively detects common and unique anomalies in the target graph. Specifically, our approach ensures deeper learning of domain-unique knowledge by decoupling the reconstruction graphs of common and unique features. Moreover, we simultaneously consider node-level and structural-level differences by transferring node and edge information from the source graph to the target graph, enabling comprehensive domain-common knowledge representation. Anomalies are detected using both common and unique features, with their synthetic score serving as the final result. Extensive experiments demonstrate the effectiveness of our approach, improving an average performance by 12.6 on the AUC-PR compared to state-of-the-art methods.
期刊介绍:
CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.