1345 lines
50 KiB
HTML
1345 lines
50 KiB
HTML
<h1 id="awesome-fraud-detection-research-papers.">Awesome Fraud
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Detection Research Papers.</h1>
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<a href="https://github.com/sindresorhus/awesome"><img
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src="https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg"
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alt="Awesome" /></a> <a href="http://makeapullrequest.com"><img
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src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square"
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src="https://img.shields.io/github/repo-size/benedekrozemberczki/awesome-fraud-detection-papers.svg"
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<p align="center">
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<img width="450" src="fraud.png">
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</p>
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<p>A curated list of fraud detection papers from the following
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conferences:</p>
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<ul>
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<li>Network Science
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<ul>
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<li><a href="http://asonam.cpsc.ucalgary.ca/2019/">ASONAM</a></li>
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<li><a href="https://www.complexnetworks.org/">COMPLEX NETWORKS</a></li>
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</ul></li>
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<li>Data Science
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<ul>
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<li><a href="http://dsaa2019.dsaa.co/">DSAA</a></li>
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</ul></li>
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<li>Natural Language Processing
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<ul>
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<li><a href="http://www.acl2019.org/EN/index.xhtml">ACL</a></li>
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</ul></li>
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<li>Data Mining
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<ul>
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<li><a href="https://www.kdd.org/">KDD</a></li>
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<li><a href="http://icdm2019.bigke.org/">ICDM</a></li>
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<li><a href="https://sigir.org/">SIGIR</a></li>
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<li><a
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href="https://www.siam.org/conferences/cm/conference/sdm20">SDM</a></li>
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<li><a href="https://www2019.thewebconf.org/">WWW</a></li>
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<li><a href="http://www.cikmconference.org/">CIKM</a></li>
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</ul></li>
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<li>Artificial Intelligence
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<ul>
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<li><a href="https://www.aaai.org/">AAAI</a></li>
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<li><a href="http://www.auai.org/">AISTATS</a></li>
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<li><a href="https://www.ijcai.org/">IJCAI</a></li>
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<li><a href="http://www.auai.org/">UAI</a></li>
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</ul></li>
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<li>Databases
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<ul>
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<li><a href="http://www.vldb.org/">VLDB</a></li>
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</ul></li>
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</ul>
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<p>Similar collections about <a
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href="https://github.com/benedekrozemberczki/awesome-graph-classification">graph
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classification</a>, <a
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href="https://github.com/benedekrozemberczki/awesome-decision-tree-papers">classification/regression
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tree</a>, <a
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href="https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers">gradient
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boosting</a>, <a
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href="https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers">Monte
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Carlo tree search</a>, and <a
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href="https://github.com/benedekrozemberczki/awesome-community-detection">community
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detection</a> papers with implementations.</p>
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<h2 id="section">2023</h2>
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<ul>
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<li><strong>Anti-Money Laundering by Group-Aware Deep Graph Learning
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(TKDE 2023)</strong>
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<ul>
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<li>Dawei Cheng, Yujia Ye, Sheng Xiang, Zhenwei Ma, Ying Zhang, Changjun
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Jiang</li>
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<li><a href="https://doi.org/10.1109/TKDE.2023.3272396">[Paper]</a></li>
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</ul></li>
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<li><strong>Semi-supervised Credit Card Fraud Detection via
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Attribute-driven Graph Representation (AAAI 2023)</strong>
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<ul>
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<li>Sheng Xiang, Mingzhi Zhu, Dawei Cheng, Enxia Li, Ruihui Zhao, Yi
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Ouyang, Ling Chen, Yefeng Zheng</li>
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<li><a
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href="https://www.xiangshengcloud.top/publication/semi-supervised-credit-card-fraud-detection-via-attribute-driven-graph-representation/Sheng-AAAI2023.pdf">[Paper]</a></li>
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<li><a href="https://github.com/finint/antifraud">[Code]</a></li>
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</ul></li>
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<li><strong>A Framework for Detecting Frauds from Extremely Few Labels
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(WSDM 2023)</strong>
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<ul>
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<li>Ya-Lin Zhang, Yi-Xuan Sun, Fangfang Fan, Meng Li, Yeyu Zhao, Wei
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Wang, Longfei Li, Jun Zhou, Jinghua Feng</li>
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<li><a
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href="https://dl.acm.org/doi/10.1145/3539597.3573022">[Paper]</a></li>
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</ul></li>
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<li><strong>Label Information Enhanced Fraud Detection against Low
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Homophily in Graphs (WWW 2023)</strong>
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<ul>
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<li>Yuchen Wang, Jinghui Zhang, Zhengjie Huang, Weibin Li, Shikun Feng,
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Ziheng Ma, Yu Sun, Dianhai Yu, Fang Dong, Jiahui Jin, Beilun Wang,
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Junzhou Luo (WWW 2023)</li>
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<li><a href="https://arxiv.org/abs/2302.10407">[Paper]</a></li>
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</ul></li>
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<li><strong>BERT4ETH: A Pre-trained Transformer for Ethereum Fraud
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Detection (WWW 2023)</strong>
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<ul>
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<li>Sihao Hu, Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He,
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Ling Liu</li>
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<li><a href="https://arxiv.org/abs/2303.18138">[Paper]</a></li>
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</ul></li>
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</ul>
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<h2 id="section-1">2022</h2>
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<ul>
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<li><strong>The Importance of Future Information in Credit Card Fraud
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Detection (AISTATS 2022)</strong>
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<ul>
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<li>Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer, Wissam
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Siblini</li>
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<li><a href="https://arxiv.org/abs/2204.05265">[Paper]</a></li>
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</ul></li>
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<li><strong>BRIGHT - Graph Neural Networks in Real-time Fraud Detection
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(CIKM 2022)</strong>
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<ul>
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<li>Mingxuan Lu, Zhichao Han, Susie Xi Rao, Zitao Zhang, Yang Zhao,
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Yinan Shan, Ramesh Raghunathan, Ce Zhang, Jiawei Jiang</li>
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<li><a href="https://arxiv.org/abs/2205.13084">[Paper]</a></li>
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</ul></li>
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<li><strong>Dual-Augment Graph Neural Network for Fraud Detection (CIKM
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2022)</strong>
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<ul>
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<li>Qiutong Li, Yanshen He, Cong Xu, Feng Wu, Jianliang Gao, Zhao
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Li</li>
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<li><a
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href="https://dl.acm.org/doi/10.1145/3511808.3557586">[Paper]</a></li>
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</ul></li>
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<li><strong>Explainable Graph-based Fraud Detection via Neural
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Meta-graph Search (CIKM 2022)</strong>
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<ul>
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<li>Zidi Qin, Yang Liu, Qing He, Xiang Ao</li>
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<li><a
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href="https://dl.acm.org/doi/abs/10.1145/3511808.3557598">[Paper]</a></li>
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</ul></li>
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<li><strong>MetaRule: A Meta-path Guided Ensemble Rule Set Learning for
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Explainable Fraud Detection (CIKM 2022)</strong>
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<ul>
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<li>Lu Yu, Meng Li, Xiaoguang Huang, Wei Zhu, Yanming Fang, Jun Zhou,
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Longfei Li</li>
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<li><a
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href="https://dl.acm.org/doi/abs/10.1145/3511808.3557641">[Paper]</a></li>
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</ul></li>
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<li><strong>User Behavior Pre-training for Online Fraud Detection (KDD
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2022)</strong>
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<ul>
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<li>Can Liu, Yuncong Gao, Li Sun, Jinghua Feng, Hao Yang, Xiang Ao</li>
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<li><a
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href="https://dl.acm.org/doi/abs/10.1145/3534678.3539126">[Paper]</a></li>
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</ul></li>
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<li><strong>Accelerated GNN Training with DGL and RAPIDS cuGraph in a
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Fraud Detection Workflow (KDD 2022)</strong>
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<ul>
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<li>Brad Rees, Xiaoyun Wang, Joe Eaton, Onur Yilmaz, Rick Ratzel,
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Dominque LaSalle</li>
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<li><a
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href="https://dl.acm.org/doi/abs/10.1145/3534678.3542603">[Paper]</a></li>
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</ul></li>
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<li><strong>A View into YouTube View Fraud (WWW 2022)</strong>
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<ul>
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<li>Dhruv Kuchhal, Frank Li</li>
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<li><a
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href="https://dl.acm.org/doi/10.1145/3485447.3512216">[Paper]</a></li>
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</ul></li>
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<li><strong>Beyond Bot Detection: Combating Fraudulent Online Survey
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Takers (WWW 2022)</strong>
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<ul>
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<li>Ziyi Zhang, Shuofei Zhu, Jaron Mink, Aiping Xiong, Linhai Song, Gang
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Wang</li>
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<li><a
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href="https://gangw.cs.illinois.edu/www22-bot.pdf">[Paper]</a></li>
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</ul></li>
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<li><strong>AUC-oriented Graph Neural Network for Fraud Detection (WWW
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2022)</strong>
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<ul>
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<li>Mengda Huang, Yang Liu, Xiang Ao, Kuan Li, Jianfeng Chi, Jinghua
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Feng, Hao Yang, Qing He</li>
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<li><a
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href="https://ponderly.github.io/pub/AOGNN_WWW2022.pdf">[Paper]</a></li>
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</ul></li>
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<li><strong>H2-FDetector: A GNN-based Fraud Detector with Homophilic and
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Heterophilic Connections (WWW 2022)</strong>
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<ul>
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<li>Fengzhao Shi, Yanan Cao, Yanmin Shang, Yuchen Zhou, Chuan Zhou, Jia
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Wu</li>
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<li><a
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href="https://dl.acm.org/doi/10.1145/3485447.3512195">[Paper]</a></li>
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</ul></li>
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<li><strong>Active Learning for Human-in-the-loop Customs Inspection
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(TKDE 2022)</strong>
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<ul>
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<li>Sundong Kim, Tung-Duong Mai, Thi Nguyen Duc Khanh, Sungwon Han,
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Sungwon Park, Karandeep Singh, Meeyoung Cha</li>
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<li><a
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href="https://ieeexplore.ieee.org/document/9695316/">[Paper]</a></li>
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<li><a
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href="https://github.com/Seondong/Customs-Fraud-Detection">[Code]</a></li>
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</ul></li>
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<li><strong>Knowledge Sharing via Domain Adaptation in Customs Fraud
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Detection (AAAI 2022)</strong>
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<ul>
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<li>Sungwon Park, Sundong Kim, Meeyoung Cha</li>
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<li><a href="https://arxiv.org/abs/2201.06759">[Paper]</a></li>
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</ul></li>
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</ul>
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<h2 id="section-2">2021</h2>
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<ul>
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<li><strong>Towards Consumer Loan Fraud Detection: Graph Neural Networks
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with Role-Constrained Conditional Random Field (AAAI 2021)</strong>
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<ul>
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<li>Bingbing Xu, Huawei Shen, Bing-Jie Sun, Rong An, Qi Cao, Xueqi
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Cheng</li>
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<li><a
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href="https://ojs.aaai.org/index.php/AAAI/article/view/16582">[Paper]</a></li>
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</ul></li>
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<li><strong>Modeling the Field Value Variations and Field Interactions
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Simultaneously for Fraud Detection (AAAI 2021)</strong>
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<ul>
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<li>Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen,
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Tianyi Zhang, Yuan Qi, Qing He</li>
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<li><a href="https://arxiv.org/abs/2008.05600">[Paper]</a></li>
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</ul></li>
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<li><strong>IFDDS: An Anti-fraud Outbound Robot (AAAI 2021)</strong>
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<ul>
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<li>Zihao Wang, Minghui Yang, Chunxiang Jin, Jia Liu, Zujie Wen,
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Saishuai Liu, Zhe Zhang</li>
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<li><a
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href="https://ojs.aaai.org/index.php/AAAI/article/view/18030">[Paper]</a></li>
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</ul></li>
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<li><strong>Modeling Heterogeneous Graph Network on Fraud Detection: A
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Community-based Framework with Attention Mechanism (CIKM 2021)</strong>
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<ul>
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<li>Li Wang, Peipei Li, Kai Xiong, Jiashu Zhao, Rui Lin</li>
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<li><a
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href="https://dl.acm.org/doi/abs/10.1145/3459637.3482277">[Paper]</a></li>
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</ul></li>
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<li><strong>Fraud Detection under Multi-Sourced Extremely Noisy
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Annotations (CIKM 2021)</strong>
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<ul>
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<li>Chuang Zhang, Qizhou Wang, Tengfei Liu, Xun Lu, Jin Hong, Bo Han,
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Chen Gong</li>
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<li><a
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href="https://gcatnjust.github.io/ChenGong/paper/zhang_cikm21.pdf">[Paper]</a></li>
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</ul></li>
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<li><strong>Adversarial Reprogramming of Pretrained Neural Networks for
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Fraud Detection (CIKM 2021)</strong>
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<ul>
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<li>Lingwei Chen, Yujie Fan, Yanfang Ye</li>
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<li><a
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href="https://dl.acm.org/doi/abs/10.1145/3459637.3482053">[Paper]</a></li>
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</ul></li>
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<li><strong>Fine-Grained Element Identification in Complaint Text of
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Internet Fraud (CIKM 2021)</strong>
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<ul>
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<li>Tong Liu, Siyuan Wang, Jingchao Fu, Lei Chen, Zhongyu Wei, Yaqi Liu,
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Heng Ye, Liaosa Xu, Weiqiang Wang, Xuanjing Huang</li>
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<li><a href="https://arxiv.org/abs/2108.08676">[Paper]</a></li>
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</ul></li>
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<li><strong>Could You Describe the Reason for the Transfer: A
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Reinforcement Learning Based Voice-Enabled Bot Protecting Customers from
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Financial Frauds (CIKM 2021)</strong>
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<ul>
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<li>Zihao Wang, Fudong Wang, Haipeng Zhang, Minghui Yang, Shaosheng Cao,
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Zujie Wen, Zhe Zhang</li>
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<li><a
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href="https://dl.acm.org/doi/abs/10.1145/3459637.3481906">[Paper]</a></li>
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</ul></li>
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<li><strong>Online Credit Payment Fraud Detection via Structure-Aware
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Hierarchical Recurrent Neural Network (IJCAI 2021)</strong>
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<ul>
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<li>Wangli Lin, Li Sun, Qiwei Zhong, Can Liu, Jinghua Feng, Xiang Ao,
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Hao Yang</li>
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<li><a
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href="https://www.ijcai.org/proceedings/2021/505">[Paper]</a></li>
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</ul></li>
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<li><strong>Intention-aware Heterogeneous Graph Attention Networks for
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Fraud Transactions Detection (KDD 2021)</strong>
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<ul>
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<li>Can Liu, Li Sun, Xiang Ao, Jinghua Feng, Qing He, Hao Yang</li>
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<li><a
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href="https://dl.acm.org/doi/10.1145/3447548.3467142">[Paper]</a></li>
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</ul></li>
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<li><strong>Live-Streaming Fraud Detection: A Heterogeneous Graph Neural
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Network Approach (KDD 2021)</strong>
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<ul>
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<li>Haishuai Wang, Zhao Li, Peng Zhang, Jiaming Huang, Pengrui Hui, Jian
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Liao, Ji Zhang, Jiajun Bu</li>
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<li><a
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href="https://dl.acm.org/doi/abs/10.1145/3447548.3467065">[Paper]</a></li>
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</ul></li>
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<li><strong>Customs Fraud Detection in the Presence of Concept Drift
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(IncrLearn@ICDM 2021)</strong>
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<ul>
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<li>Tung-Duong Mai, Kien Hoang, Aitolkyn Baigutanova, Gaukhartas Alina,
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Sundong Kim</li>
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<li><a href="https://arxiv.org/abs/2109.14155">[Paper]</a></li>
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</ul></li>
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<li><strong>Pick and Choose: A GNN-based Imbalanced Learning Approach
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for Fraud Detection (WWW 2021)</strong>
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<ul>
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<li>Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang,
|
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Qing He</li>
|
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<li><a
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href="https://dl.acm.org/doi/abs/10.1145/3442381.3449989">[Paper]</a></li>
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</ul></li>
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</ul>
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<h2 id="section-3">2020</h2>
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<ul>
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<li><strong>Spatio-Temporal Attention-Based Neural Network for Credit
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Card Fraud Detection (AAAI 2020)</strong>
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<ul>
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<li>Dawei Cheng, Sheng Xiang, Chencheng Shang, Yiyi Zhang, Fangzhou
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Yang, Liqing Zhang</li>
|
||
<li><a
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||
href="https://aaai.org/Papers/AAAI/2020GB/AISI-ChengD.87.pdf">[Paper]</a></li>
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</ul></li>
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<li><strong>FlowScope: Spotting Money Laundering Based on Graphs (AAAI
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2020)</strong>
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<ul>
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<li>Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi,
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Bryan Hooi, He Huang, Xueqi Cheng</li>
|
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<li><a
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href="https://shenghua-liu.github.io/papers/aaai2020cr-flowscope.pdf">[Paper]</a></li>
|
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<li><a href="https://github.com/aplaceof/FlowScope">[Code]</a></li>
|
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</ul></li>
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<li><strong>Enhancing Graph Neural Network-based Fraud Detectors against
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Camouflaged Fraudsters (CIKM 2020)</strong>
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<ul>
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<li>Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, Philip S.
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Yu</li>
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<li><a href="https://arxiv.org/abs/2008.08692">[Paper]</a></li>
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<li><a href="https://github.com/YingtongDou/CARE-GNN">[Code]</a></li>
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</ul></li>
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<li><strong>Loan Default Analysis with Multiplex Graph Learning (CIKM
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2020)</strong>
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<ul>
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<li>Binbin Hu, Zhiqiang Zhang, Jun Zhou, Jingli Fang, Quanhui Jia,
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Yanming Fang, Quan Yu, Yuan Qi</li>
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<li><a
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href="https://www.researchgate.net/publication/343626706_Loan_Default_Analysis_with_Multiplex_Graph_Learning">[Paper]</a></li>
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</ul></li>
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<li><strong>Error-Bounded Graph Anomaly Loss for GNNs (CIKM
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2020)</strong>
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<ul>
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<li>Tong Zhao, Chuchen Deng, Kaifeng Yu, Tianwen Jiang, Daheng Wang,
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Meng Jiang</li>
|
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<li><a
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href="http://www.meng-jiang.com/pubs/gal-cikm20/gal-cikm20-paper.pdf">[Paper]</a></li>
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<li><a
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href="https://github.com/zhao-tong/Graph-Anomaly-Loss">[Code]</a></li>
|
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</ul></li>
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<li><strong>BotSpot: A Hybrid Learning Framework to Uncover Bot Install
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Fraud in Mobile Advertising (CIKM 2020)</strong>
|
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<ul>
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<li>Tianjun Yao, Qing Li, Shangsong Liang, Yadong Zhu</li>
|
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<li><a
|
||
href="https://dl.acm.org/doi/pdf/10.1145/3340531.3412690">[Paper]</a></li>
|
||
<li><a
|
||
href="https://github.com/akakeigo2020/CIKM-Applied_Research-2150">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Early Fraud Detection with Augmented Graph Learning (DLG@KDD
|
||
2020)</strong>
|
||
<ul>
|
||
<li>Tong Zhao, Bo Ni, Wenhao Yu, Meng Jiang</li>
|
||
<li><a
|
||
href="http://www.meng-jiang.com/pubs/earlyfraud-dlg20/earlyfraud-dlg20-paper.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>NAG: Neural Feature Aggregation Framework for Credit Card
|
||
Fraud Detection (ICDM 2020)</strong>
|
||
<ul>
|
||
<li>Kanishka Ghosh Dastidar, Johannes Jurgovsky, Wissam Siblini, Liyun
|
||
He-Guelton, Michael Granitzer</li>
|
||
<li><a
|
||
href="https://www.computer.org/csdl/proceedings-article/icdm/2020/831600a092/1r54A3Sb2yk">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Heterogeneous Mini-Graph Neural Network and Its Application
|
||
to Fraud Invitation Detection (ICDM 2020)</strong>
|
||
<ul>
|
||
<li>Yong-Nan Zhu, Xiaotian Luo, Yu-Feng Li, Bin Bu, Kaibo Zhou, Wenbin
|
||
Zhang, Mingfan Lu</li>
|
||
<li><a
|
||
href="https://cs.nju.edu.cn/liyf/paper/icdm20-hmgnn.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Collaboration Based Multi-Label Propagation for Fraud
|
||
Detection (IJCAI 2020)</strong>
|
||
<ul>
|
||
<li>Haobo Wang, Zhao Li, Jiaming Huang, Pengrui Hui, Weiwei Liu, Tianlei
|
||
Hu, Gang Chen</li>
|
||
<li><a
|
||
href="https://www.ijcai.org/Proceedings/2020/343">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>The Behavioral Sign of Account Theft: Realizing Online
|
||
Payment Fraud Alert (IJCAI 2020)</strong>
|
||
<ul>
|
||
<li>Cheng Wang</li>
|
||
<li><a
|
||
href="https://www.ijcai.org/Proceedings/2020/0636.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Federated Meta-Learning for Fraudulent Credit Card Detection
|
||
(IJCAI 2020)</strong>
|
||
<ul>
|
||
<li>Wenbo Zheng, Lan Yan, Chao Gou, Fei-Yue Wang</li>
|
||
<li><a
|
||
href="https://www.ijcai.org/Proceedings/2020/642">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Robust Spammer Detection by Nash Reinforcement Learning (KDD
|
||
2020)</strong>
|
||
<ul>
|
||
<li>Yingtong Dou, Guixiang Ma, Philip S. Yu, Sihong Xie</li>
|
||
<li><a href="https://arxiv.org/abs/2006.06069">[Paper]</a></li>
|
||
<li><a href="https://github.com/YingtongDou/Nash-Detect">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>DATE: Dual Attentive Tree-aware Embedding for Customs Fraud
|
||
Detection (KDD 2020)</strong>
|
||
<ul>
|
||
<li>Sundong Kim, Yu-Che Tsai, Karandeep Singh, Yeonsoo Choi, Etim Ibok,
|
||
Cheng-Te Li, Meeyoung Cha</li>
|
||
<li><a
|
||
href="https://seondong.github.io/assets/papers/2020_KDD_DATE.pdf">[Paper]</a></li>
|
||
<li><a
|
||
href="https://github.com/Roytsai27/Dual-Attentive-Tree-aware-Embedding">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Fraud Transactions Detection via Behavior Tree with Local
|
||
Intention Calibration (KDD 2020)</strong>
|
||
<ul>
|
||
<li>Can Liu, Qiwei Zhong, Xiang Ao, Li Sun, Wangli Lin, Jinghua Feng,
|
||
Qing He, Jiayu Tang</li>
|
||
<li><a
|
||
href="https://dl.acm.org/doi/pdf/10.1145/3394486.3403354">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Interleaved Sequence RNNs for Fraud Detection (KDD
|
||
2020)</strong>
|
||
<ul>
|
||
<li>Bernardo Branco, Pedro Abreu, Ana Sofia Gomes, Mariana S. C.
|
||
Almeida, João Tiago Ascensão, Pedro Bizarro</li>
|
||
<li><a href="https://arxiv.org/abs/2002.05988">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>GCN-Based User Representation Learning for Unifying Robust
|
||
Recommendation and Fraudster Detection (SIGIR 2020)</strong>
|
||
<ul>
|
||
<li>Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi
|
||
Huang, Lizhen Cui</li>
|
||
<li><a href="https://arxiv.org/abs/2005.10150">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Alleviating the Inconsistency Problem of Applying Graph
|
||
Neural Network to Fraud Detection (SIGIR 2020)</strong>
|
||
<ul>
|
||
<li>Zhiwei Liu, Yingtong Dou, Philip S. Yu, Yutong Deng, Hao Peng</li>
|
||
<li><a href="https://arxiv.org/abs/2005.00625">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/DGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Friend or Faux: Graph-Based Early Detection of Fake Accounts
|
||
on Social Networks (WWW 2020)</strong>
|
||
<ul>
|
||
<li>Adam Breuer, Roee Eilat, Udi Weinsberg</li>
|
||
<li><a href="https://arxiv.org/abs/2004.04834">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Financial Defaulter Detection on Online Credit Payment via
|
||
Multi-view Attributed Heterogeneous Information Network (WWW
|
||
2020)</strong>
|
||
<ul>
|
||
<li>Qiwei Zhong, Yang Liu, Xiang Ao, Binbin Hu, Jinghua Feng, Jiayu
|
||
Tang, Qing He</li>
|
||
<li><a
|
||
href="https://dl.acm.org/doi/abs/10.1145/3366423.3380159">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>ASA: Adversary Situation Awareness via Heterogeneous Graph
|
||
Convolutional Networks (WWW 2020)</strong>
|
||
<ul>
|
||
<li>Rui Wen, Jianyu Wang, Chunming Wu, Jian Xiong</li>
|
||
<li><a
|
||
href="https://dl.acm.org/doi/10.1145/3366424.3391266">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Modeling Users’ Behavior Sequences with Hierarchical
|
||
Explainable Network for Cross-domain Fraud Detection (WWW 2020)</strong>
|
||
<ul>
|
||
<li>Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Xi
|
||
Gu, Qing He</li>
|
||
<li><a
|
||
href="https://dl.acm.org/doi/fullHtml/10.1145/3366423.3380172">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-4">2019</h2>
|
||
<ul>
|
||
<li><strong>SliceNDice: Mining Suspicious Multi-attribute Entity Groups
|
||
with Multi-view Graphs (DSAA 2019)</strong>
|
||
<ul>
|
||
<li>Hamed Nilforoshan, Neil Shah</li>
|
||
<li><a href="https://arxiv.org/abs/1908.07087">[Paper]</a></li>
|
||
<li><a href="https://github.com/hamedn/SliceNDice">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>FARE: Schema-Agnostic Anomaly Detection in Social Event Logs
|
||
(DSAA 2019)</strong>
|
||
<ul>
|
||
<li>Neil Shah</li>
|
||
<li><a
|
||
href="http://nshah.net/publications/FARE.DSAA.19.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Cash-Out User Detection Based on Attributed Heterogeneous
|
||
Information Network with a Hierarchical Attention Mechanism (AAAI
|
||
2019)</strong>
|
||
<ul>
|
||
<li>Binbin Hu, Zhiqiang Zhang, Chuan Shi, Jun Zhou, Xiaolong Li, Yuan
|
||
Qi</li>
|
||
<li><a
|
||
href="https://aaai.org/ojs/index.php/AAAI/article/view/3884">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/DGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>GeniePath: Graph Neural Networks with Adaptive Receptive
|
||
Paths (AAAI 2019)</strong>
|
||
<ul>
|
||
<li>Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song,
|
||
Yuan Qi</li>
|
||
<li><a href="https://arxiv.org/abs/1802.00910">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/DGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>SAFE: A Neural Survival Analysis Model for Fraud Early
|
||
Detection (AAAI 2019)</strong>
|
||
<ul>
|
||
<li>Panpan Zheng, Shuhan Yuan, Xintao Wu</li>
|
||
<li><a href="https://arxiv.org/abs/1809.04683v2">[Paper]</a></li>
|
||
<li><a href="https://github.com/PanpanZheng/SAFE">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>One-Class Adversarial Nets for Fraud Detection (AAAI
|
||
2019)</strong>
|
||
<ul>
|
||
<li>Panpan Zheng, Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu</li>
|
||
<li><a href="https://arxiv.org/abs/1803.01798">[Paper]</a></li>
|
||
<li><a href="https://github.com/ILoveAI2019/OCAN">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Uncovering Download Fraud Activities in Mobile App Markets
|
||
(ASONAM 2019)</strong>
|
||
<ul>
|
||
<li>Yingtong Dou, Weijian Li, Zhirong Liu, Zhenhua Dong, Jiebo Luo,
|
||
Philip S. Yu</li>
|
||
<li><a href="https://arxiv.org/pdf/1907.03048.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Spam Review Detection with Graph Convolutional Networks
|
||
(CIKM 2019)</strong>
|
||
<ul>
|
||
<li>Ao Li, Zhou Qin, Runshi Liu, Yiqun Yang, Dong Li</li>
|
||
<li><a href="https://arxiv.org/abs/1908.10679">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/DGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Key Player Identification in Underground Forums Over
|
||
Attributed Heterogeneous Information Network Embedding Framework (CIKM
|
||
2019)</strong>
|
||
<ul>
|
||
<li>Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, Chuan Shi</li>
|
||
<li><a
|
||
href="http://mason.gmu.edu/~lzhao9/materials/papers/lp0110-zhangA.pdf">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/DGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>CatchCore: Catching Hierarchical Dense Subtensor (ECML-PKDD
|
||
2019)</strong>
|
||
<ul>
|
||
<li>Wenjie Feng, Shenghua Liu, Huawei Shen, and Xueqi Cheng</li>
|
||
<li><a
|
||
href="https://shenghua-liu.github.io/papers/pkdd2019-catchcore.pdf">[Paper]</a></li>
|
||
<li><a href="https://github.com/wenchieh/catchcore">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Spotting Collective Behaviour of Online Frauds in Customer
|
||
Reviews (IJCAI 2019)</strong>
|
||
<ul>
|
||
<li>Sarthika Dhawan, Siva Charan Reddy Gangireddy, Shiv Kumar, Tanmoy
|
||
Chakraborty</li>
|
||
<li><a href="https://arxiv.org/abs/1905.13649">[Paper]</a></li>
|
||
<li><a href="https://github.com/LCS2-IIITD/DeFrauder">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>A Semi-Supervised Graph Attentive Network for Fraud
|
||
Detection (ICDM 2019)</strong>
|
||
<ul>
|
||
<li>Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming
|
||
Fang, Quan Yu, Jun Zhou, Shuang Yang, and Qi Yuan</li>
|
||
<li><a href="https://arxiv.org/abs/2003.01171">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/DGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>EigenPulse: Detecting Surges in Large Streaming Graphs with
|
||
Row Augmentation (PAKDD 2019)</strong>
|
||
<ul>
|
||
<li>Jiabao Zhang, Shenghua Liu, Wenjian Yu, Wenjie Feng, Xueqi
|
||
Cheng</li>
|
||
<li><a
|
||
href="https://shenghua-liu.github.io/papers/pakdd2019-eigenpulse.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Uncovering Insurance Fraud Conspiracy with Network Learning
|
||
(SIGIR 2019)</strong>
|
||
<ul>
|
||
<li>Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang,
|
||
Yuan Qi</li>
|
||
<li><a
|
||
href="https://dl.acm.org/citation.cfm?id=3331372">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>A Contrast Metric for Fraud Detection in Rich Graphs (TKDE
|
||
2019)</strong>
|
||
<ul>
|
||
<li>Shenghua Liu, Bryan Hooi, Christos Faloutsos</li>
|
||
<li><a
|
||
href="https://shenghua-liu.github.io/papers/tkde2019-constrastsusp_holoscope.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Think Outside the Dataset: Finding Fraudulent Reviews using
|
||
Cross-Dataset Analysis (WWW 2019)</strong>
|
||
<ul>
|
||
<li>Shirin Nilizadeh, Hojjat Aghakhani, Eric Gustafson, Christopher
|
||
Kruegel, Giovanni Vigna</li>
|
||
<li><a
|
||
href="https://www.researchgate.net/publication/333060486_Think_Outside_the_Dataset_Finding_Fraudulent_Reviews_using_Cross-Dataset_Analysis">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Securing the Deep Fraud Detector in Large-Scale E-Commerce
|
||
Platform via Adversarial Machine Learning Approach (WWW 2019)</strong>
|
||
<ul>
|
||
<li>Qingyu Guo, Zhao Li, Bo An, Pengrui Hui, Jiaming Huang, Long Zhang,
|
||
Mengchen Zhao</li>
|
||
<li><a
|
||
href="https://www.ntu.edu.sg/home/boan/papers/WWW19.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>No Place to Hide: Catching Fraudulent Entities in Tensors
|
||
(WWW 2019)</strong>
|
||
<ul>
|
||
<li>Yikun Ban, Xin Liu, Ling Huang, Yitao Duan, Xue Liu, Wei Xu</li>
|
||
<li><a href="https://arxiv.org/pdf/1810.06230.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>FdGars: Fraudster Detection via Graph Convolutional Networks
|
||
in Online App Review System (WWW 2019)</strong>
|
||
<ul>
|
||
<li>Rui Wen, Jianyu Wang and Yu Huang</li>
|
||
<li><a
|
||
href="https://dl.acm.org/citation.cfm?id=3316586">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/DGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-5">2018</h2>
|
||
<ul>
|
||
<li><strong>Heterogeneous Graph Neural Networks for Malicious Account
|
||
Detection (CIKM 2018)</strong>
|
||
<ul>
|
||
<li>Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le
|
||
Song</li>
|
||
<li><a
|
||
href="https://dl.acm.org/doi/10.1145/3269206.3272010">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/DGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Reinforcement Mechanism Design for Fraudulent Behaviour in
|
||
e-Commerce (AAAI 2018)</strong>
|
||
<ul>
|
||
<li>Qingpeng Cai, Aris Filos-Ratsikas, Pingzhong Tang, Yiwei Zhang</li>
|
||
<li><a
|
||
href="https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16650">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Adapting to Concept Drift in Credit Card Transaction Data
|
||
Streams Using Contextual Bandits and Decision Trees (AAAI 2018)</strong>
|
||
<ul>
|
||
<li>Dennis J. N. J. Soemers, Tim Brys, Kurt Driessens, Mark H. M.
|
||
Winands, Ann Nowé</li>
|
||
<li><a
|
||
href="https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16183/16394">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Nextgen AML: Distributed Deep Learning Based Language
|
||
Technologies to Augment Anti Money Laundering Investigation(ACL
|
||
2018)</strong>
|
||
<ul>
|
||
<li>Jingguang Han, Utsab Barman, Jeremiah Hayes, Jinhua Du, Edward
|
||
Burgin, Dadong Wan</li>
|
||
<li><a href="https://www.aclweb.org/anthology/P18-4007">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Preserving Privacy of Fraud Detection Rule Sharing Using
|
||
Intel’s SGX (CIKM 2018)</strong>
|
||
<ul>
|
||
<li>Daniel Deutch, Yehonatan Ginzberg, Tova Milo</li>
|
||
<li><a
|
||
href="https://www.researchgate.net/publication/328439345_Preserving_Privacy_of_Fraud_Detection_Rule_Sharing_Using_Intel%27s_SGX">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Deep Structure Learning for Fraud Detection (ICDM
|
||
2018)</strong>
|
||
<ul>
|
||
<li>Haibo Wang, Chuan Zhou, Jia Wu, Weizhen Dang, Xingquan Zhu, Jilong
|
||
Wang</li>
|
||
<li><a
|
||
href="https://www.researchgate.net/publication/330030140_Deep_Structure_Learning_for_Fraud_Detection">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Learning Sequential Behavior Representations for Fraud
|
||
Detection (ICDM 2018)</strong>
|
||
<ul>
|
||
<li>Jia Guo, Guannan Liu, Yuan Zuo, Junjie Wu</li>
|
||
<li><a
|
||
href="https://www.researchgate.net/publication/330028902_Learning_Sequential_Behavior_Representations_for_Fraud_Detection">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Impression Allocation for Combating Fraud in E-commerce Via
|
||
Deep Reinforcement Learning with Action Norm Penalty (IJCAI
|
||
2018)</strong>
|
||
<ul>
|
||
<li>Mengchen Zhao, Zhao Li, Bo An, Haifeng Lu, Yifan Yang, Chen Chu</li>
|
||
<li><a
|
||
href="https://www.ijcai.org/proceedings/2018/0548.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Tax Fraud Detection for Under-Reporting Declarations Using
|
||
an Unsupervised Machine Learning Approach (KDD 2018)</strong>
|
||
<ul>
|
||
<li>Daniel de Roux, Boris Perez, Andrés Moreno, María-Del-Pilar
|
||
Villamil, César Figueroa</li>
|
||
<li><a
|
||
href="https://www.kdd.org/kdd2018/accepted-papers/view/tax-fraud-detection-for-under-reporting-declarations-using-an-unsupervised-">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Collective Fraud Detection Capturing Inter-Transaction
|
||
Dependency (KDD 2018)</strong>
|
||
<ul>
|
||
<li>Bokai Cao, Mia Mao, Siim Viidu, Philip Yu</li>
|
||
<li><a
|
||
href="http://proceedings.mlr.press/v71/cao18a.html">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Fraud Detection with Density Estimation Trees (KDD
|
||
2018)</strong>
|
||
<ul>
|
||
<li>Fraud Detection with Density Estimation Trees</li>
|
||
<li><a
|
||
href="http://proceedings.mlr.press/v71/ram18a/ram18a.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Real-time Constrained Cycle Detection in Large Dynamic
|
||
Graphs (VLDB 2018)</strong>
|
||
<ul>
|
||
<li>Xiafei Qiu, Wubin Cen, Zhengping Qian, You Peng, Ying Zhang, Xuemin
|
||
Lin, Jingren Zhou</li>
|
||
<li><a
|
||
href="http://www.vldb.org/pvldb/vol11/p1876-qiu.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>REV2: Fraudulent User Prediction in Rating Platforms (WSDM
|
||
2018)</strong>
|
||
<ul>
|
||
<li>Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos
|
||
Faloutsos, V. S. Subrahmanian</li>
|
||
<li><a
|
||
href="https://cs.stanford.edu/~srijan/pubs/rev2-wsdm18.pdf">[Paper]</a></li>
|
||
<li><a href="https://cs.stanford.edu/~srijan/rev2/">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Exposing Search and Advertisement Abuse Tactics and
|
||
Infrastructure of Technical Support Scammers (WWW 2018)</strong>
|
||
<ul>
|
||
<li>Bharat Srinivasan, Athanasios Kountouras, Najmeh Miramirkhani,
|
||
Monjur Alam, Nick Nikiforakis, Manos Antonakakis, Mustaque Ahamad</li>
|
||
<li><a
|
||
href="https://www.securitee.org/files/tss_www2018.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-6">2017</h2>
|
||
<ul>
|
||
<li><strong>ZooBP: Belief Propagation for Heterogeneous Networks (VLDB
|
||
2017)</strong>
|
||
<ul>
|
||
<li>Dhivya Eswaran, Stephan Gunnemann, Christos Faloutsos, Disha
|
||
Makhija, Mohit Kumar</li>
|
||
<li><a
|
||
href="http://www.vldb.org/pvldb/vol10/p625-eswaran.pdf">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/UGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Behavioral Analysis of Review Fraud: Linking Malicious
|
||
Crowdsourcing to Amazon and Beyond (AAAI 2017)</strong>
|
||
<ul>
|
||
<li>Parisa Kaghazgaran, James Caverlee, Majid Alfifi</li>
|
||
<li><a
|
||
href="https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15659">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Detection of Money Laundering Groups: Supervised Learning on
|
||
Small Networks (AAAI 2017)</strong>
|
||
<ul>
|
||
<li>David Savage, Qingmai Wang, Xiuzhen Zhang, Pauline Chou, Xinghuo
|
||
Yu</li>
|
||
<li><a href="https://arxiv.org/pdf/1608.00708.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Spectrum-based Deep Neural Networks for Fraud Detection
|
||
(CIKM 2017)</strong>
|
||
<ul>
|
||
<li>Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu</li>
|
||
<li><a href="https://arxiv.org/abs/1706.00891">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>HoloScope: Topology-and-Spike Aware Fraud Detection (CIKM
|
||
2017)</strong>
|
||
<ul>
|
||
<li>Shenghua Liu, Bryan Hooi, Christos Faloutsos</li>
|
||
<li><a href="https://arxiv.org/abs/1705.02505">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>The Many Faces of Link Fraud (ICDM 2017)</strong>
|
||
<ul>
|
||
<li>Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos</li>
|
||
<li><a href="https://arxiv.org/abs/1704.01420">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>HitFraud: A Broad Learning Approach for Collective Fraud
|
||
Detection in Heterogeneous Information Networks (ICDM 2017)</strong>
|
||
<ul>
|
||
<li>Bokai Cao, Mia Mao, Siim Viidu, Philip S. Yu</li>
|
||
<li><a href="https://arxiv.org/abs/1709.04129">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>GANG: Detecting Fraudulent Users in Online Social Networks
|
||
via Guilt-by-Association on Directed Graphs (ICDM 2017)</strong>
|
||
<ul>
|
||
<li>Binghui Wang, Neil Zhenqiang Gong, Hao Fu</li>
|
||
<li><a
|
||
href="https://ieeexplore.ieee.org/document/8215519">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/UGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Improving Card Fraud Detection Through Suspicious Pattern
|
||
Discovery (IEA/AIE 2017)</strong>
|
||
<ul>
|
||
<li>Fabian Braun, Olivier Caelen, Evgueni N. Smirnov, Steven Kelk,
|
||
Bertrand Lebichot:</li>
|
||
<li><a
|
||
href="http://www.oliviercaelen.be/doc/GBSSCCFDS.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Online Reputation Fraud Campaign Detection in User Ratings
|
||
(IJCAI 2017)</strong>
|
||
<ul>
|
||
<li>Chang Xu, Jie Zhang, Zhu Sun</li>
|
||
<li><a
|
||
href="https://www.ijcai.org/proceedings/2017/0541.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Uncovering Unknown Unknowns in Financial Services Big Data
|
||
by Unsupervised Methodologies: Present and Future trends (KDD
|
||
2017)</strong>
|
||
<ul>
|
||
<li>Gil Shabat, David Segev, Amir Averbuch</li>
|
||
<li><a
|
||
href="http://proceedings.mlr.press/v71/shabat18a.html">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>PD-FDS: Purchase Density based Online Credit Card Fraud
|
||
Detection System (KDD 2017)</strong>
|
||
<ul>
|
||
<li>Youngjoon Ki, Ji Won Yoon</li>
|
||
<li><a
|
||
href="http://proceedings.mlr.press/v71/ki18a/ki18a.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>HiDDen: Hierarchical Dense Subgraph Detection with
|
||
Application to Financial Fraud Detection (SDM 2017)</strong>
|
||
<ul>
|
||
<li>Si Zhang, Dawei Zhou, Mehmet Yigit Yildirim, Scott Alcorn, Jingrui
|
||
He, Hasan Davulcu, Hanghang Tong</li>
|
||
<li><a
|
||
href="http://www.public.asu.edu/~hdavulcu/SDM17.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-7">2016</h2>
|
||
<ul>
|
||
<li><strong>A Fraud Resilient Medical Insurance Claim System (AAAI
|
||
2016)</strong>
|
||
<ul>
|
||
<li>Yuliang Shi, Chenfei Sun, Qingzhong Li, Lizhen Cui, Han Yu, Chunyan
|
||
Miao</li>
|
||
<li><a
|
||
href="https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11813">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>A Graph-Based, Semi-Supervised, Credit Card Fraud Detection
|
||
System (COMPLEX NETWORKS 2016)</strong>
|
||
<ul>
|
||
<li>Bertrand Lebichot, Fabian Braun, Olivier Caelen, Marco Saerens</li>
|
||
<li><a
|
||
href="http://www.oliviercaelen.be/doc/IEAAIE_2017_Finalversion-PDF_39.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>FRAUDAR: Bounding Graph Fraud in the Face of Camouflage (KDD
|
||
2016)</strong>
|
||
<ul>
|
||
<li>Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin,
|
||
Christos Faloutsos</li>
|
||
<li><a
|
||
href="https://www.andrew.cmu.edu/user/bhooi/papers/fraudar_kdd16.pdf">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/UGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Identifying Anomalies in Graph Streams Using Change
|
||
Detection (KDD 2016)</strong>
|
||
<ul>
|
||
<li>William Eberle and Lawrence Holde</li>
|
||
<li><a
|
||
href="http://www.mlgworkshop.org/2016/paper/MLG2016_paper_12.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>FairPlay: Fraud and Malware Detection in Google Play (SDM
|
||
2016)</strong>
|
||
<ul>
|
||
<li>Mahmudur Rahman, Mizanur Rahman, Bogdan Carbunar, Duen Horng
|
||
Chau</li>
|
||
<li><a href="https://arxiv.org/abs/1703.02002">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
|
||
(SDM 2016)</strong>
|
||
<ul>
|
||
<li>Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu,
|
||
Mohit Kumar, Disha Makhija, Christos Faloutsos</li>
|
||
<li><a
|
||
href="https://www.andrew.cmu.edu/user/bhooi/papers/birdnest_sdm16.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Understanding the Detection of View Fraud in Video Content
|
||
Portals (WWW 2016)</strong>
|
||
<ul>
|
||
<li>Miriam Marciel, Rubén Cuevas, Albert Banchs, Roberto Gonzalez,
|
||
Stefano Traverso, Mohamed Ahmed, Arturo Azcorra</li>
|
||
<li><a
|
||
href="https://dl.acm.org/citation.cfm?id=2882980">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-8">2015</h2>
|
||
<ul>
|
||
<li><strong>Toward An Intelligent Agent for Fraud Detection — The CFE
|
||
Agent (AAAI 2015)</strong>
|
||
<ul>
|
||
<li>Joe Johnson</li>
|
||
<li><a
|
||
href="https://www.aaai.org/ocs/index.php/FSS/FSS15/paper/download/11664/11485">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Graph Analysis for Detecting Fraud, Waste, and Abuse in
|
||
Healthcare Data (AAAI 2015)</strong>
|
||
<ul>
|
||
<li>Juan Liu, Eric Bier, Aaron Wilson, Tomonori Honda, Kumar Sricharan,
|
||
Leilani Gilpin, John Alexis Guerra Gómez, Daniel Davies</li>
|
||
<li><a
|
||
href="https://pdfs.semanticscholar.org/1ea7/125b789ef938bffe10c7588e6b071c4ff73c.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Robust System for Identifying Procurement Fraud (AAAI
|
||
2015)</strong>
|
||
<ul>
|
||
<li>Amit Dhurandhar, Rajesh Kumar Ravi, Bruce Graves, Gopikrishnan
|
||
Maniachari, Markus Ettl</li>
|
||
<li><a
|
||
href="https://pdfs.semanticscholar.org/27af/c9ec453ae0cf9e55f4032ff688cb70c2a61e.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Fraud Transaction Recognition: A Money Flow Network Approach
|
||
(CIKM 2015)</strong>
|
||
<ul>
|
||
<li>Renxin Mao, Zhao Li, Jinhua Fu</li>
|
||
<li><a
|
||
href="https://dl.acm.org/citation.cfm?id=2806647">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Towards Collusive Fraud Detection in Online Reviews (ICDM
|
||
2015)</strong>
|
||
<ul>
|
||
<li>Chang Xu, Jie Zhang</li>
|
||
<li><a
|
||
href="https://ieeexplore.ieee.org/document/7373434">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Catch the Black Sheep: Unified Framework for Shilling Attack
|
||
Detection Based on Fraudulent Action Propagation (IJCAI 2015)</strong>
|
||
<ul>
|
||
<li>Yongfeng Zhang, Yunzhi Tan, Min Zhang, Yiqun Liu, Tat-Seng Chua,
|
||
Shaoping Ma</li>
|
||
<li><a
|
||
href="https://www.ijcai.org/Proceedings/15/Papers/341.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Collective Opinion Spam Detection: Bridging Review Networks
|
||
and Metadata (KDD 2015)</strong>
|
||
<ul>
|
||
<li>Shebuti Rayana, Leman Akoglu</li>
|
||
<li><a
|
||
href="https://www.andrew.cmu.edu/user/lakoglu/pubs/15-kdd-collectiveopinionspam.pdf">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/UGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Graph-Based User Behavior Modeling: From Prediction to Fraud
|
||
Detection (KDD 2015)</strong>
|
||
<ul>
|
||
<li>Alex Beutel, Leman Akoglu, Christos Faloutsos</li>
|
||
<li><a
|
||
href="https://www.cs.cmu.edu/~abeutel/kdd2015_tutorial/tutorial.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>FrauDetector: A Graph-Mining-based Framework for Fraudulent
|
||
Phone Call Detection (KDD 2015)</strong>
|
||
<ul>
|
||
<li>Vincent S. Tseng, Jia-Ching Ying, Che-Wei Huang, Yimin Kao, Kuan-Ta
|
||
Chen</li>
|
||
<li><a
|
||
href="http://repository.ncku.edu.tw/bitstream/987654321/166322/1/4010204000-000004_1.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>A Framework for Intrusion Detection Based on Frequent
|
||
Subgraph Mining (SDM 2015)</strong>
|
||
<ul>
|
||
<li>Vitali Herrera-Semenets, Niusvel Acosta-Mendoza, Andres
|
||
Gago-Alonso</li>
|
||
<li><a
|
||
href="https://www.researchgate.net/publication/271839253_A_Framework_for_Intrusion_Detection_based_on_Frequent_Subgraph_Mining">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Crowd Fraud Detection in Internet Advertising (WWW
|
||
2015)</strong>
|
||
<ul>
|
||
<li>Tian Tian, Jun Zhu, Fen Xia, Xin Zhuang, Tong Zhang</li>
|
||
<li><a
|
||
href="http://www.www2015.it/documents/proceedings/proceedings/p1100.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-9">2014</h2>
|
||
<ul>
|
||
<li><strong>Spotting Suspicious Link Behavior with fBox: An Adversarial
|
||
Perspective (ICDM 2014)</strong>
|
||
<ul>
|
||
<li>Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos</li>
|
||
<li><a href="https://arxiv.org/pdf/1410.3915.pdf">[Paper]</a></li>
|
||
<li><a href="https://github.com/safe-graph/UGFraud">[Code]</a></li>
|
||
</ul></li>
|
||
<li><strong>Fraudulent Support Telephone Number Identification Based on
|
||
Co-Occurrence Information on the Web (AAAI 2014)</strong>
|
||
<ul>
|
||
<li>Xin Li, Yiqun Liu, Min Zhang, Shaoping Ma</li>
|
||
<li><a
|
||
href="https://pdfs.semanticscholar.org/2733/1f48c87736ea12b9edec062e384d3bd58f88.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Corporate Residence Fraud Detection (KDD 2014)</strong>
|
||
<ul>
|
||
<li>Enric Junqué de Fortuny, Marija Stankova, Julie Moeyersoms, Bart
|
||
Minnaert, Foster J. Provost, David Martens</li>
|
||
<li><a
|
||
href="http://delivery.acm.org/10.1145/2630000/2623333/p1650-fortuny.pdf?ip=129.215.164.203&id=2623333&acc=ACTIVE%20SERVICE&key=C2D842D97AC95F7A%2EEB9E991028F4E1F1%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1559048806_f1a6f763ef7088a4fb4b1a4ff94856f8">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Graphical Models for Identifying Fraud and Waste in
|
||
Healthcare Claims (SDM 2014)</strong>
|
||
<ul>
|
||
<li>Peder A. Olsen, Ramesh Natarajan, Sholom M. Weiss</li>
|
||
<li><a
|
||
href="https://epubs.siam.org/doi/abs/10.1137/1.9781611973440.66">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Improving Credit Card Fraud Detection with Calibrated
|
||
Probabilities (SDM 2014)</strong>
|
||
<ul>
|
||
<li>Alejandro Correa Bahnsen, Aleksandar Stojanovic, Djamila Aouada,
|
||
Björn E. Ottersten</li>
|
||
<li><a
|
||
href="https://pdfs.semanticscholar.org/9241/ef2a2f6638eafeffd0056736c0f46f9aa083.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Large Graph Mining: Patterns, Cascades, Fraud Detection, and
|
||
Algorithms (WWW 2014)</strong>
|
||
<ul>
|
||
<li>Christos Faloutsos</li>
|
||
<li><a
|
||
href="http://wwwconference.org/proceedings/www2014/proceedings/p1.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-10">2013</h2>
|
||
<ul>
|
||
<li><strong>Opinion Fraud Detection in Online Reviews by Network Effects
|
||
(AAAI 2013)</strong>
|
||
<ul>
|
||
<li>Leman Akoglu, Rishi Chandy, Christos Faloutsos</li>
|
||
<li><a
|
||
href="https://www.researchgate.net/publication/279905898_Opinion_fraud_detection_in_online_reviews_by_network_effects">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Using Social Network Knowledge for Detecting Spider
|
||
Constructions in Social Security Fraud (ASONAM 2013)</strong>
|
||
<ul>
|
||
<li>Véronique Van Vlasselaer, Jan Meskens, Dries Van Dromme, Bart
|
||
Baesens</li>
|
||
<li><a
|
||
href="https://ieeexplore.ieee.org/document/6785796">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Ranking Fraud Detection for Mobile Apps: a Holistic View
|
||
(CIKM 2013)</strong>
|
||
<ul>
|
||
<li>Hengshu Zhu, Hui Xiong, Yong Ge, Enhong Chen</li>
|
||
<li><a href="http://dm.ustc.edu.cn/zhu-cikm13.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Using Co-Visitation Networks for Detecting Large Scale
|
||
Online Display Advertising Exchange Fraud (KDD 2013)</strong>
|
||
<ul>
|
||
<li>Ori Stitelman, Claudia Perlich, Brian Dalessandro, Rod Hook, Troy
|
||
Raeder, Foster J. Provost</li>
|
||
<li><a
|
||
href="http://chbrown.github.io/kdd-2013-usb/kdd/p1240.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Adaptive Adversaries: Building Systems to Fight Fraud and
|
||
Cyber Intruders (KDD 2013)</strong>
|
||
<ul>
|
||
<li>Ari Gesher</li>
|
||
<li><a
|
||
href="https://dl.acm.org/citation.cfm?id=2491134">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Anomaly, Event, and Fraud Detection in Large Network
|
||
Datasets (WSDM 2013)</strong>
|
||
<ul>
|
||
<li>Leman Akoglu, Christos Faloutsos</li>
|
||
<li><a
|
||
href="https://www.andrew.cmu.edu/user/lakoglu/wsdm13/13-wsdm-tutorial.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-11">2012</h2>
|
||
<ul>
|
||
<li><strong>Fraud Detection: Methods of Analysis for Hypergraph Data
|
||
(ASONAM 2012)</strong>
|
||
<ul>
|
||
<li>Anna Leontjeva, Konstantin Tretyakov, Jaak Vilo, and Taavi
|
||
Tamkivi</li>
|
||
<li><a
|
||
href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6425618">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Online Modeling of Proactive Moderation System for Auction
|
||
Fraud Detection (WWW 2012)</strong>
|
||
<ul>
|
||
<li>Liang Zhang, Jie Yang, Belle L. Tseng</li>
|
||
<li><a
|
||
href="http://www.chennaisunday.com/Java%202012%20Base%20Paper/Online%20Modeling%20of%20Proactive%20Moderation%20System%20for%20Auction%20Fraud%20Detection.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-12">2011</h2>
|
||
<ul>
|
||
<li><strong>A Machine-Learned Proactive Moderation System for Auction
|
||
Fraud Detection (CIKM 2011)</strong>
|
||
<ul>
|
||
<li>Liang Zhang, Jie Yang, Wei Chu, Belle L. Tseng</li>
|
||
<li><a
|
||
href="http://www.gatsby.ucl.ac.uk/~chuwei/paper/p2501-zhang.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>A Taxi Driving Fraud Detection System (ICDM 2011)</strong>
|
||
<ul>
|
||
<li>Yong Ge, Hui Xiong, Chuanren Liu, Zhi-Hua Zhou</li>
|
||
<li><a
|
||
href="https://ieeexplore.ieee.org/document/6137222">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Utility-Based Fraud Detection (IJCAI 2011)</strong>
|
||
<ul>
|
||
<li>Luís Torgo, Elsa Lopes</li>
|
||
<li><a
|
||
href="https://www.ijcai.org/Proceedings/11/Papers/255.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>A Pattern Discovery Approach to Retail Fraud Detection (KDD
|
||
2011)</strong>
|
||
<ul>
|
||
<li>Prasad Gabbur, Sharath Pankanti, Quanfu Fan, Hoang Trinh</li>
|
||
<li><a
|
||
href="http://www2.engr.arizona.edu/~pgsangam/gabbur_kdd_11.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-13">2010</h2>
|
||
<ul>
|
||
<li><strong>Hunting for the Black Swan: Risk Mining from Text (ACL
|
||
2010)</strong>
|
||
<ul>
|
||
<li>JL Leidner, F Schilder</li>
|
||
<li><a href="https://www.aclweb.org/anthology/P10-4010">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Fraud Detection by Generating Positive Samples for
|
||
Classification from Unlabeled Data (ACL 2010)</strong>
|
||
<ul>
|
||
<li>Levente Kocsis, Andras George</li>
|
||
<li><a
|
||
href="http://www.szit.bme.hu/~gya/publications/KocsisGyorgy.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-14">2009</h2>
|
||
<ul>
|
||
<li><strong>SVM-based Credit Card Fraud Detection with Reject Cost and
|
||
Class-Dependent Error Cost (PAKDD 2009)</strong>
|
||
<ul>
|
||
<li>En-hui Zheng,Chao Zou,Jian Sun, Le Chen</li>
|
||
<li><a
|
||
href="https://www.semanticscholar.org/paper/SVM-Based-Cost-sensitive-Classification-Algorithm-Zheng-Zou/bcae06626ccd453925ef040a1edb5cbb10b862ef">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>An Approach for Automatic Fraud Detection in the Insurance
|
||
Domain (AAAI 2009)</strong>
|
||
<ul>
|
||
<li>Alexander Widder, Rainer v. Ammon, Gerit Hagemann, Dirk
|
||
Schönfeld</li>
|
||
<li><a
|
||
href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.325.3231&rep=rep1&type=pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-15">2007</h2>
|
||
<ul>
|
||
<li><strong>Relational Data Pre-Processing Techniques for Improved
|
||
Securities Fraud Detection (KDD 2007)</strong>
|
||
<ul>
|
||
<li>Andrew S. Fast, Lisa Friedland, Marc E. Maier, Brian J. Taylor,
|
||
David D. Jensen, Henry G. Goldberg, John Komoroske</li>
|
||
<li><a
|
||
href="https://dl.acm.org/citation.cfm?id=1281192.1281293">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Uncovering Fraud in Direct Marketing Data with a Fraud
|
||
Auditing Case Builder (PKDD 2007)</strong>
|
||
<ul>
|
||
<li>Fletcher Lu</li>
|
||
<li><a
|
||
href="https://link.springer.com/chapter/10.1007/978-3-540-74976-9_56">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Netprobe: A Fast and Scalable System for Fraud Detection in
|
||
Online Auction Networks (WWW 2007)</strong>
|
||
<ul>
|
||
<li>Shashank Pandit, Duen Horng Chau, Samuel Wang, Christos
|
||
Faloutsos</li>
|
||
<li><a
|
||
href="http://www.cs.cmu.edu/~christos/PUBLICATIONS/netprobe-www07.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-16">2006</h2>
|
||
<ul>
|
||
<li><strong>Data Mining Approaches to Criminal Career Analysis (ICDM
|
||
2006)</strong>
|
||
<ul>
|
||
<li>Jeroen S. De Bruin, Tim K. Cocx, Walter A. Kosters, Jeroen F. J.
|
||
Laros, Joost N. Kok</li>
|
||
<li><a
|
||
href="https://ieeexplore.ieee.org/document/4053045">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Large Scale Detection of Irregularities in Accounting Data
|
||
(ICDM 2006)</strong>
|
||
<ul>
|
||
<li>Stephen Bay, Krishna Kumaraswamy, Markus G. Anderle, Rohit Kumar,
|
||
David M. Steier</li>
|
||
<li><a
|
||
href="https://ieeexplore.ieee.org/document/4053036">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Camouflaged Fraud Detection in Domains with Complex
|
||
Relationships (KDD 2006)</strong>
|
||
<ul>
|
||
<li>Sankar Virdhagriswaran, Gordon Dakin</li>
|
||
<li><a
|
||
href="https://dl.acm.org/citation.cfm?id=1150532">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Detecting Fraudulent Personalities in Networks of Online
|
||
Auctioneers (PKDD 2006)</strong>
|
||
<ul>
|
||
<li>Duen Horng Chau, Shashank Pandit, Christos Faloutsos</li>
|
||
<li><a
|
||
href="http://www.cs.cmu.edu/~dchau/papers/auction_fraud_pkdd06.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-17">2005</h2>
|
||
<ul>
|
||
<li><strong>Technologies to Defeat Fraudulent Schemes Related to Email
|
||
Requests (AAAI 2005)</strong>
|
||
<ul>
|
||
<li>Edoardo Airoldi, Bradley Malin, and Latanya Sweeney</li>
|
||
<li><a
|
||
href="http://www.aaai.org/Library/Symposia/Spring/2005/ss05-01-023.php">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>AI Technologies to Defeat Identity Theft Vulnerabilities
|
||
(AAAI 2005)</strong>
|
||
<ul>
|
||
<li>Latanya Sweeney</li>
|
||
<li><a
|
||
href="https://dataprivacylab.org/dataprivacy/projects/idangel/paper1.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Detecting Fraud in Health Insurance Data: Learning to Model
|
||
Incomplete Benford’s Law Distributions (ECML 2005)</strong>
|
||
<ul>
|
||
<li>Fletcher Lu, J. Efrim Boritz</li>
|
||
<li><a
|
||
href="https://faculty.uoit.ca/fletcherlu/LuECML05.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Using Relational Knowledge Discovery to Prevent Securities
|
||
Fraud (KDD 2005)</strong>
|
||
<ul>
|
||
<li>Jennifer Neville, Özgür Simsek, David D. Jensen, John Komoroske,
|
||
Kelly Palmer, Henry G. Goldberg</li>
|
||
<li><a
|
||
href="https://www.cs.purdue.edu/homes/neville/papers/neville-et-al-kdd2005.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-18">2003</h2>
|
||
<ul>
|
||
<li><strong>Applying Data Mining in Investigating Money Laundering
|
||
Crimes (KDD 2003)</strong>
|
||
<ul>
|
||
<li>Zhongfei (Mark) Zhang, John J. Salerno, Philip S. Yu</li>
|
||
<li><a
|
||
href="https://pdfs.semanticscholar.org/9124/b61d48b7e52008c7fd5fac1b7eac38474581.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-19">2000</h2>
|
||
<ul>
|
||
<li><strong>Document Classification and Visualisation to Support the
|
||
Investigation of Suspected Fraud (PKDD 2000)</strong>
|
||
<ul>
|
||
<li>Johan Hagman, Domenico Perrotta, Ralf Steinberger, and Aristi de
|
||
Varfis</li>
|
||
<li><a
|
||
href="https://pdfs.semanticscholar.org/9124/b61d48b7e52008c7fd5fac1b7eac38474581.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-20">1999</h2>
|
||
<ul>
|
||
<li><strong>Statistical Challenges to Inductive Inference in Linked
|
||
Data. (AISTATS 1999)</strong>
|
||
<ul>
|
||
<li>David Jensen</li>
|
||
<li><a
|
||
href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.589.1445&rep=rep1&type=pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-21">1998</h2>
|
||
<ul>
|
||
<li><strong>Toward Scalable Learning with Non-Uniform Class and Cost
|
||
Distributions: A Case Study in Credit Card Fraud Detection (KDD
|
||
1998)</strong>
|
||
<ul>
|
||
<li>Phillip K Chan, Salvatore J Stolfo</li>
|
||
<li><a
|
||
href="https://pdfs.semanticscholar.org/6e19/3366945bf3bd72d5ba906e3982ac4d8ae874.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Call-Based Fraud Detection in Mobile Communication Networks
|
||
Using a Hierarchical Regime-Switching Model (NIPS 1998)</strong>
|
||
<ul>
|
||
<li>Jaakko Hollmén, Volker Tresp</li>
|
||
<li><a
|
||
href="https://papers.nips.cc/paper/1505-call-based-fraud-detection-in-mobile-communication-networks-using-a-hierarchical-regime-switching-model.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-22">1997</h2>
|
||
<ul>
|
||
<li><strong>Detection of Mobile Phone Fraud Using Supervised Neural
|
||
Networks: A First Prototype (ICANN 1997)</strong>
|
||
<ul>
|
||
<li>Yves Moreau, Herman Verrelst, Joos Vandewalle</li>
|
||
<li><a
|
||
href="https://link.springer.com/content/pdf/10.1007%2FBFb0020294.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Prospective Assessment of AI Technologies for Fraud
|
||
Detection: A Case Study (AAAI 1997)</strong>
|
||
<ul>
|
||
<li>David Jensen</li>
|
||
<li><a
|
||
href="https://pdfs.semanticscholar.org/0efe/8a145cc4d52e8769bb1d13142326a154624f.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
<li><strong>Credit Card Fraud Detection Using Meta-Learning: Issues and
|
||
Initial Results (AAAI 1997)</strong>
|
||
<ul>
|
||
<li>Salvatore J. Stolfo, David W. Fan, Wenke Lee and Andreas L.
|
||
Prodromidis</li>
|
||
<li><a
|
||
href="https://pdfs.semanticscholar.org/29b3/e330e0045e5da71cc1d333bed24b7a4670f8.pdf">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h2 id="section-23">1995</h2>
|
||
<ul>
|
||
<li><strong>Fraud: Uncollectible Debt Detection Using a Bayesian Network
|
||
Based Learning System: A Rare Binary Outcome with Mixed Data Structures
|
||
(UAI 1995)</strong>
|
||
<ul>
|
||
<li>Kazuo J. Ezawa, Til Schuermann</li>
|
||
<li><a href="https://arxiv.org/abs/1302.4945">[Paper]</a></li>
|
||
</ul></li>
|
||
</ul>
|
||
<hr />
|
||
<p><strong>License</strong></p>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/benedekrozemberczki/awesome-fraud-detection-papers/blob/master/LICENSE">CC0
|
||
Universal</a></li>
|
||
</ul>
|
||
<p><a
|
||
href="https://github.com/benedekrozemberczki/awesome-fraud-detection-papers">frauddetectionpapers.md
|
||
Github</a></p>
|