{"type":"ScholarlyArticle","title":"Collaboration Based Multi-Label Propagation for Fraud Detection","authors":[{"name":"Haobo Wang","slug":"haobo-wang","given":"Haobo","family":"Wang","affiliation":"Key Lab of Intelligent Computing Based Big Data of Zhejiang Province , Zhejiang University; Alibaba Group , Hangzhou , China"},{"name":"Zhao Li","slug":"zhao-li","given":"Zhao","family":"Li","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Jiaming Huang","slug":"jiaming-huang","given":"Jiaming","family":"Huang","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Pengrui Hui","slug":"pengrui-hui","given":"Pengrui","family":"Hui","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Weiwei Liu","slug":"weiwei-liu","given":"Weiwei","family":"Liu","affiliation":"School of Computer Science , Wuhan University"},{"name":"Tianlei Hu","slug":"tianlei-hu","given":"Tianlei","family":"Hu","affiliation":"Key Lab of Intelligent Computing Based Big Data of Zhejiang Province , Zhejiang University"},{"name":"Gang Chen","slug":"gang-chen","given":"Gang","family":"Chen","affiliation":"Key Lab of Intelligent Computing Based Big Data of Zhejiang Province , Zhejiang University"}],"abstract":"Detecting fraud users, who fraudulently promote certain target items, is a challenging issue faced by e-commerce platforms. Generally, many fraud users have different spam behaviors simultaneously, e.g. spam transactions, clicks, reviews and so on. Existing solutions have two main limitations: 1) the correlations among multiple spam behaviors are neglected; 2) large-scale computations are intractable when dealing with an enormous user set. To remedy these problems, this work proposes a collaboration based multi-label propagation (CMLP) algorithm. We first introduce a general-purpose version that involves collaboration technique to exploit label correlations. Specifically, it breaks the final prediction into two parts: 1) its own prediction part; 2) the prediction of others, i.e. collaborative part. Then, to accelerate it on large-scale e-commerce data, we propose a heterogeneous graph based variant that detects communities on the user-item graph directly. Both theoretical analysis and empirical results clearly validate the effectiveness and scalability of our proposals.","doi":"10.24963/ijcai.2020/343","url":"https://ijcai.org/proceedings/2020/343","pdf":"https://ijcai.org/proceedings/2020/0343.pdf","markdown":"https://ijcai.org/proceedings/2020/343.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/343","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":"Machine Learning","track":"Main track","number":343,"pages":{"first":2477,"last":2483},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25208/file_25208.m3u8","length_sec":296,"provider":"hls"}],"references":[{"position":1,"raw":"[Adamic and Adar, 2003] Lada A. 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