{"type":"ScholarlyArticle","title":"Beyond Homophily: Robust Graph Anomaly Detection via Neural Sparsification","authors":[{"name":"Zheng Gong","slug":"zheng-gong","given":"Zheng","family":"Gong","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence"},{"name":"Guifeng Wang","slug":"guifeng-wang","given":"Guifeng","family":"Wang","affiliation":"Huawei Technologies Co Ltd"},{"name":"Ying Sun","slug":"ying-sun","given":"Ying","family":"Sun","affiliation":"Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Qi Liu","slug":"qi-liu","given":"Qi","family":"Liu","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence"},{"name":"Yuting Ning","slug":"yuting-ning","given":"Yuting","family":"Ning","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence"},{"name":"Hui Xiong","slug":"hui-xiong","given":"Hui","family":"Xiong","affiliation":"Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Jingyu Peng","slug":"jingyu-peng","given":"Jingyu","family":"Peng","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence"}],"abstract":"Recently, graph-based anomaly detection (GAD) has attracted rising attention due to its effectiveness in identifying anomalies in relational and structured data. Unfortunately, the performance of most existing GAD methods suffers from the inherent structural noises of graphs induced by hidden anomalies connected with considerable benign nodes. In this work, we propose SparseGAD, a novel GAD framework that sparsifies the structures of target graphs to effectively reduce noises and collaboratively learns node representations. It then robustly detects anomalies by uncovering the underlying dependency among node pairs in terms of homophily and heterophily, two essential connection properties of GAD. Extensive experiments on real-world datasets of GAD demonstrate that the proposed framework achieves significantly better detection quality compared with the state-of-the-art methods, even when the graph is heavily attacked. Code will be available at https://github.com/KellyGong/SparseGAD.git.","doi":"10.24963/ijcai.2023/234","url":"https://ijcai.org/proceedings/2023/234","pdf":"https://ijcai.org/proceedings/2023/0234.pdf","markdown":"https://ijcai.org/proceedings/2023/234.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/234","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":234,"pages":{"first":2104,"last":2113},"keywords":["Data Mining: DM: Mining graphs","Data Mining: DM: Anomaly/outlier detection","Data Mining: DM: Applications"],"videos":[],"references":[{"position":1,"raw":"[Bo et al., 2021] Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen. 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