{"type":"ScholarlyArticle","title":"Radar: Residual Analysis for Anomaly Detection in Attributed Networks","authors":[{"name":"Jundong Li","slug":"jundong-li","given":"Jundong","family":"Li","affiliation":""},{"name":"Harsh Dani","slug":"harsh-dani","given":"Harsh","family":"Dani","affiliation":""},{"name":"Xia Hu","slug":"xia-hu","given":"Xia","family":"Hu","affiliation":"‡ Department of Computer Science and Engineering , Texas A&M University , USA"},{"name":"Huan Liu","slug":"huan-liu","given":"Huan","family":"Liu","affiliation":"† Computer Science and Engineering , Arizona State University , USA"}],"abstract":"Attributed networks are pervasive in different domains, ranging from social networks, gene regulatory networks to financial transaction networks. This kind of rich network representation presents challenges for anomaly detection due to the heterogeneity of two data representations. A vast majority of existing algorithms assume certain properties of anomalies are given a prior. Since various types of anomalies in real-world attributed networks co-exist, the assumption that priori knowledge regarding anomalies is available does not hold. In this paper, we investigate the problem of anomaly detection in attributed networks generally from a residual analysis perspective, which has been shown to be effective in traditional anomaly detection problems. However, it is a non-trivial task in attributed networks as interactions among instances complicate the residual modeling process. Methodologically, we propose a learning framework to characterize the residuals of attribute information and its coherence with network information for anomaly detection. By learning and analyzing the residuals, we detect anomalies whose behaviors are singularly different from the majority. Experiments on real datasets show the effectiveness and generality of the proposed framework.","doi":"10.24963/ijcai.2017/299","url":"https://ijcai.org/proceedings/2017/299","pdf":"https://ijcai.org/proceedings/2017/0299.pdf","markdown":"https://ijcai.org/proceedings/2017/299.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/299","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":299,"pages":{"first":2152,"last":2158},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Data Mining","Machine Learning: Relational Learning"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal and Yu, 2001] Charu C Aggarwal and Philip S Yu. 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