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Bipartite Dynamic Representations for Abuse Detection

Authors:

Andrew Z. Wang,

Karthik Subbian,

Jure LeskovecAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 3638 - 3648

https://doi.org/10.1145/3447548.3467141

Published: 14 August 2021 Publication History

Abstract

Abusive behavior in online retail websites and communities threatens the experience of regular community members. Such behavior often takes place within a complex, dynamic, and large-scale network of users interacting with items. Detecting abuse is challenging due to the scarcity of labeled abuse instances and complexity of combining temporal and network patterns while operating at a massive scale. Previous approaches to dynamic graph modeling either do not scale, do not effectively generalize from a few labeled instances, or compromise performance for scalability. Here we present BiDyn, a general method to detect abusive behavior in dynamic bipartite networks at scale, while generalizing from limited training labels. BiDyn develops an efficient hybrid RNN-GNN architecture trained via a novel stacked ensemble training scheme. We also propose a novel pre-training framework for dynamic graphs that helps to achieve superior performance at scale. Our approach outperforms recent large-scale dynamic graph baselines in an abuse classification task by up to 14% AUROC while requiring 10x less memory per training batch in both open and proprietary datasets.

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Cited By

Xue JYabe TTsubouchi KMa JUkkusuri S(2025)Predicting Individual Irregular Mobility via Web Search-Driven Bipartite Graph Neural NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348754937:2(851-864)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3487549
Fang ZSun QWang QChen LGao Y(2025)Distributed Temporal Graph Neural Network Learning over Large-Scale Dynamic GraphsDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_4(51-66)Online publication date: 11-Jan-2025
https://doi.org/10.1007/978-981-97-5779-4_4
Wang HYang RXiao XBaeza-Yates RBonchi F(2024)Effective Edge-wise Representation Learning in Edge-Attributed Bipartite GraphsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671805(3081-3091)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671805
Show More Cited By

Index Terms

Bipartite Dynamic Representations for Abuse Detection
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Content ranking

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cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 August 2021

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KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2021

Virtual Event, Singapore

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Xue JYabe TTsubouchi KMa JUkkusuri S(2025)Predicting Individual Irregular Mobility via Web Search-Driven Bipartite Graph Neural NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348754937:2(851-864)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3487549
Fang ZSun QWang QChen LGao Y(2025)Distributed Temporal Graph Neural Network Learning over Large-Scale Dynamic GraphsDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_4(51-66)Online publication date: 11-Jan-2025
https://doi.org/10.1007/978-981-97-5779-4_4
Wang HYang RXiao XBaeza-Yates RBonchi F(2024)Effective Edge-wise Representation Learning in Edge-Attributed Bipartite GraphsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671805(3081-3091)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671805
Cheng KPeng LWang PYe JSun LDu BBaeza-Yates RBonchi F(2024)DyGKT: Dynamic Graph Learning for Knowledge TracingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671773(409-420)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671773
Lu YTsai YLi CChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Burstiness-aware Bipartite Graph Neural Networks for Fraudulent User Detection on Rating PlatformsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651475(834-837)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651475
Yang SLi CChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Detecting Illicit Food Factories from Chemical Declaration Data via Graph-aware Self-supervised Contrastive Anomaly RankingProceedings of the ACM Web Conference 202410.1145/3589334.3648138(4501-4511)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3648138
Wu JHu RLi DRen LHuang ZZang Y(2024)Beyond the individualNeural Networks10.1016/j.neunet.2023.10.019169:C(20-31)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.neunet.2023.10.019
Li QNi HYou T(2024)Anomaly Detection with Dual-Channel Heterogeneous Graph Based on Hypersphere LearningInformation Sciences10.1016/j.ins.2024.121242(121242)Online publication date: Jul-2024
https://doi.org/10.1016/j.ins.2024.121242
Ren LZang YHu RLi DWu JHuan ZHu J(2024)Do not ignore heterogeneity and heterophily: Multi-network collaborative telecom fraud detectionExpert Systems with Applications10.1016/j.eswa.2024.124974257(124974)Online publication date: Dec-2024
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Hu JHu RWang ZLi DWu JRen LZang YHuang ZWang MEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Collaborative Fraud Detection: How Collaboration Impacts Fraud DetectionProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613780(8891-8899)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613780
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