{"type":"ScholarlyArticle","title":"Anomaly Subgraph Detection through High-Order Sampling Contrastive Learning","authors":[{"name":"Ying Sun","slug":"ying-sun","given":"Ying","family":"Sun","affiliation":"College of Intelligence and Computing , Tianjin University , China; School of Digital and Intelligent Industry , Inner Mongolia University of Science and Technology , China"},{"name":"Wenjun Wang","slug":"wenjun-wang","given":"Wenjun","family":"Wang","affiliation":"College of Intelligence and Computing , Tianjin University , China; Yazhou Bay Innovation Institute , Hainan Tropical Ocean University , China"},{"name":"Nannan Wu","slug":"nannan-wu","given":"Nannan","family":"Wu","affiliation":"College of Intelligence and Computing , Tianjin University , China"},{"name":"Chunlong Bao","slug":"chunlong-bao","given":"Chunlong","family":"Bao","affiliation":"School of Digital and Intelligent Industry , Inner Mongolia University of Science and Technology , China"}],"abstract":"Anomaly subgraph detection is a crucial task in various real-world applications, including identifying high-risk areas, detecting river pollution, and monitoring disease outbreaks. Early traditional graph-based methods can obtain high-precision detection results in scenes with small-scale graphs and obvious anomaly features. Most existing anomaly detection methods based on deep learning primarily concentrate on identifying anomalies at the node level, while neglecting to detect anomaly groups in the internal structure. In this paper, we propose a novel end-to-end  Graph Neural Network (GNN) based anomaly subgraph detection approach(ASD-HC) in graph-structured data. 1)We propose a high-order neighborhood sampling strategy to construct our node and k-order neighbor-subgraph instance pairs. 2)Anomaly features of nodes are captured through a self-supervised contrastive learning model. 3) Detecting the maximum connected anomaly subgraph is performed by integrating the Non-parameter Graph Scan statistics and a Random Walk module. We evaluate ASD-HC against five state-of-the-art baselines using five benchmark datasets. ASD-HC outperforms the baselines by over 13.01% in AUC score. Various experiments demonstrate that our approach effectively detects anomaly subgraphs within large-scale graphs.","doi":"10.24963/ijcai.2024/261","url":"https://ijcai.org/proceedings/2024/261","pdf":"https://ijcai.org/proceedings/2024/0261.pdf","markdown":"https://ijcai.org/proceedings/2024/261.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/261","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":261,"pages":{"first":2362,"last":2369},"keywords":["Data Mining: DM: Mining graphs","Data Mining: DM: Anomaly/outlier detection","Machine Learning: ML: Feature extraction, selection and dimensionality reduction","Machine Learning: ML: Self-supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Akoglu et al., 2015] Leman Akoglu, Hanghang Tong, and Danai Koutra. 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