{"type":"ScholarlyArticle","title":"Inductive Anomaly Detection on Attributed Networks","authors":[{"name":"Kaize Ding","slug":"kaize-ding","given":"Kaize","family":"Ding","affiliation":"Computer Science and Engineering , Arizona State University , USA"},{"name":"Jundong Li","slug":"jundong-li","given":"Jundong","family":"Li","affiliation":"Electrical and Computer Engineering , University of Virginia , USA; Computer Science &"},{"name":"Nitin Agarwal","slug":"nitin-agarwal","given":"Nitin","family":"Agarwal","affiliation":"Information Science , University of Arkansas Little Rock , USA; School of Data Science , University of Virginia , USA"},{"name":"Huan Liu","slug":"huan-liu","given":"Huan","family":"Liu","affiliation":"Computer Science and Engineering , Arizona State University , USA"}],"abstract":"Anomaly detection on attributed networks has attracted a surge of research attention due to its broad applications in various high-impact domains, such as security, finance, and healthcare. Nonetheless, most of the existing efforts do not naturally generalize to unseen nodes, leading to the fact that people have to retrain the detection model from scratch when dealing with newly observed data. In this study, we propose to tackle the problem of inductive anomaly detection on attributed networks with a novel unsupervised framework: Aegis (adversarial graph differentiation networks). Specifically, we design a new graph neural layer to learn anomaly-aware node representations and further employ generative adversarial learning to detect anomalies among new data. Extensive experiments on various attributed networks demonstrate the efficacy of the proposed approach.","doi":"10.24963/ijcai.2020/179","url":"https://ijcai.org/proceedings/2020/179","pdf":"https://ijcai.org/proceedings/2020/0179.pdf","markdown":"https://ijcai.org/proceedings/2020/179.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/179","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":179,"pages":{"first":1288,"last":1294},"keywords":["Data Mining: Mining Graphs, Semi Structured Data, Complex Data","Machine Learning Applications: Applications of Unsupervised Learning","Data Mining: Applications"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25544/file_25544.m3u8","length_sec":271,"provider":"hls"}],"references":[{"position":1,"raw":"[Bojchevski and Günnemann, 2017] Aleksandar Bojchevski and Stephan Günnemann. 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