[38;5;12m                                                                                                  [39m[38;2;255;187;0m[1m[4mAwesome Fraud Detection Research Papers.[0m
[38;5;14m[1m![0m[38;5;12mAwesome[39m[38;5;14m[1m (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)[0m[38;5;12m (https://github.com/sindresorhus/awesome)[39m
[38;5;14m[1m![0m[38;5;12mPRs Welcome[39m[38;5;14m[1m (https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)[0m[38;5;12m (http://makeapullrequest.com)[39m
[38;5;14m[1m![0m[38;5;12mrepo size[39m[38;5;14m[1m (https://img.shields.io/github/repo-size/benedekrozemberczki/awesome-fraud-detection-papers.svg)[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-fraud-detection-papers/archive/master.zip)[39m
[38;5;12m![39m[38;5;14m[1mLicense[0m[38;5;12m [39m[38;5;12m(https://img.shields.io/github/license/benedekrozemberczki/awesome-fraud-detection-papers.svg?color=blue)[39m[38;5;12m [39m[38;5;14m[1m![0m[38;5;12mbenedekrozemberczki[39m[38;5;14m[1m [0m[38;5;14m[1m(https://img.shields.io/twitter/follow/benrozemberczki?style=social&logo=twitter)[0m[38;5;12m [39m
[38;5;12m(https://twitter.com/intent/follow?screen_name=benrozemberczki)[39m

[38;5;12m  [39m

[38;5;12mA curated list of fraud detection papers from the following conferences:[39m

[38;5;12m- Network Science[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mASONAM[0m[38;5;12m (http://asonam.cpsc.ucalgary.ca/2019/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCOMPLEX NETWORKS[0m[38;5;12m (https://www.complexnetworks.org/)[39m
[38;5;12m- Data Science[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDSAA[0m[38;5;12m (http://dsaa2019.dsaa.co/)[39m
[38;5;12m- Natural Language Processing[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mACL[0m[38;5;12m (http://www.acl2019.org/EN/index.xhtml)[39m
[38;5;12m- Data Mining[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKDD[0m[38;5;12m (https://www.kdd.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mICDM[0m[38;5;12m (http://icdm2019.bigke.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSIGIR[0m[38;5;12m (https://sigir.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSDM[0m[38;5;12m (https://www.siam.org/conferences/cm/conference/sdm20)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mWWW[0m[38;5;12m (https://www2019.thewebconf.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCIKM[0m[38;5;12m (http://www.cikmconference.org/)[39m
[38;5;12m- Artificial Intelligence[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAAAI[0m[38;5;12m (https://www.aaai.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAISTATS[0m[38;5;12m (http://www.auai.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIJCAI[0m[38;5;12m (https://www.ijcai.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mUAI[0m[38;5;12m (http://www.auai.org/)[39m
[38;5;12m- Databases[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVLDB[0m[38;5;12m (http://www.vldb.org/)[39m

[38;5;12mSimilar[39m[38;5;12m [39m[38;5;12mcollections[39m[38;5;12m [39m[38;5;12mabout[39m[38;5;12m [39m[38;5;14m[1mgraph[0m[38;5;14m[1m [0m[38;5;14m[1mclassification[0m[38;5;12m [39m[38;5;12m(https://github.com/benedekrozemberczki/awesome-graph-classification),[39m[38;5;12m [39m[38;5;14m[1mclassification/regression[0m[38;5;14m[1m [0m[38;5;14m[1mtree[0m[38;5;12m [39m[38;5;12m(https://github.com/benedekrozemberczki/awesome-decision-tree-papers),[39m[38;5;12m [39m[38;5;14m[1mgradient[0m[38;5;14m[1m [0m
[38;5;14m[1mboosting[0m[38;5;12m [39m[38;5;12m(https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers),[39m[38;5;12m [39m[38;5;14m[1mMonte[0m[38;5;14m[1m [0m[38;5;14m[1mCarlo[0m[38;5;14m[1m [0m[38;5;14m[1mtree[0m[38;5;14m[1m [0m[38;5;14m[1msearch[0m[38;5;12m [39m[38;5;12m(https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers),[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;14m[1mcommunity[0m[38;5;14m[1m [0m[38;5;14m[1mdetection[0m[38;5;12m [39m
[38;5;12m(https://github.com/benedekrozemberczki/awesome-community-detection)[39m[38;5;12m [39m[38;5;12mpapers[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mimplementations.[39m


[38;2;255;187;0m[4m2023[0m
[38;5;12m- [39m[38;5;14m[1mAnti-Money Laundering by Group-Aware Deep Graph Learning (TKDE 2023)[0m
[38;5;12m  - Dawei Cheng, Yujia Ye, Sheng Xiang, Zhenwei Ma, Ying Zhang, Changjun Jiang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://doi.org/10.1109/TKDE.2023.3272396)[39m

[38;5;12m- [39m[38;5;14m[1mSemi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation (AAAI 2023)[0m
[38;5;12m  - Sheng Xiang, Mingzhi Zhu, Dawei Cheng, Enxia Li, Ruihui Zhao, Yi Ouyang, Ling Chen, Yefeng Zheng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.xiangshengcloud.top/publication/semi-supervised-credit-card-fraud-detection-via-attribute-driven-graph-representation/Sheng-AAAI2023.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/finint/antifraud)[39m

[38;5;12m- [39m[38;5;14m[1mA Framework for Detecting Frauds from Extremely Few Labels (WSDM 2023)[0m
[38;5;12m  - Ya-Lin Zhang, Yi-Xuan Sun, Fangfang Fan, Meng Li, Yeyu Zhao, Wei Wang, Longfei Li, Jun Zhou, Jinghua Feng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/10.1145/3539597.3573022)[39m
[38;5;12m [39m
[38;5;12m- [39m[38;5;14m[1mLabel Information Enhanced Fraud Detection against Low Homophily in Graphs (WWW 2023)[0m
[38;5;12m  - Yuchen Wang, Jinghui Zhang, Zhengjie Huang, Weibin Li, Shikun Feng, Ziheng Ma, Yu Sun, Dianhai Yu, Fang Dong, Jiahui Jin, Beilun Wang, Junzhou Luo (WWW 2023)[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2302.10407)[39m

[38;5;12m- [39m[38;5;14m[1mBERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection (WWW 2023)[0m
[38;5;12m  - Sihao Hu, Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He, Ling Liu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2303.18138)[39m

[38;2;255;187;0m[4m2022[0m

[38;5;12m- [39m[38;5;14m[1mThe Importance of Future Information in Credit Card Fraud Detection (AISTATS 2022)[0m
[38;5;12m  - Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer, Wissam Siblini[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2204.05265)[39m

[38;5;12m- [39m[38;5;14m[1mBRIGHT - Graph Neural Networks in Real-time Fraud Detection (CIKM 2022)[0m
[38;5;12m  - Mingxuan Lu, Zhichao Han, Susie Xi Rao, Zitao Zhang, Yang Zhao, Yinan Shan, Ramesh Raghunathan, Ce Zhang, Jiawei Jiang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2205.13084)[39m

[38;5;12m- [39m[38;5;14m[1mDual-Augment Graph Neural Network for Fraud Detection (CIKM 2022)[0m
[38;5;12m  - Qiutong Li, Yanshen He, Cong Xu, Feng Wu, Jianliang Gao, Zhao Li[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/10.1145/3511808.3557586)[39m

[38;5;12m- [39m[38;5;14m[1mExplainable Graph-based Fraud Detection via Neural Meta-graph Search (CIKM 2022)[0m
[38;5;12m  - Zidi Qin, Yang Liu, Qing He, Xiang Ao[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3511808.3557598)[39m

[38;5;12m- [39m[38;5;14m[1mMetaRule: A Meta-path Guided Ensemble Rule Set Learning for Explainable Fraud Detection (CIKM 2022)[0m
[38;5;12m  - Lu Yu, Meng Li, Xiaoguang Huang, Wei Zhu, Yanming Fang, Jun Zhou, Longfei Li[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3511808.3557641)[39m

[38;5;12m- [39m[38;5;14m[1mUser Behavior Pre-training for Online Fraud Detection (KDD 2022)[0m
[38;5;12m  - Can Liu, Yuncong Gao, Li Sun, Jinghua Feng, Hao Yang, Xiang Ao[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3534678.3539126)[39m

[38;5;12m- [39m[38;5;14m[1mAccelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow (KDD 2022)[0m
[38;5;12m  - Brad Rees, Xiaoyun Wang, Joe Eaton, Onur Yilmaz, Rick Ratzel, Dominque LaSalle[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3534678.3542603)[39m

[38;5;12m- [39m[38;5;14m[1mA View into YouTube View Fraud (WWW 2022)[0m
[38;5;12m  - Dhruv Kuchhal, Frank Li[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/10.1145/3485447.3512216)[39m

[38;5;12m- [39m[38;5;14m[1mBeyond Bot Detection: Combating Fraudulent Online Survey Takers (WWW 2022)[0m
[38;5;12m  - Ziyi Zhang, Shuofei Zhu, Jaron Mink, Aiping Xiong, Linhai Song, Gang Wang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://gangw.cs.illinois.edu/www22-bot.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mAUC-oriented Graph Neural Network for Fraud Detection (WWW 2022)[0m
[38;5;12m  - Mengda Huang, Yang Liu, Xiang Ao, Kuan Li, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ponderly.github.io/pub/AOGNN_WWW2022.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mH2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic Connections (WWW 2022)[0m
[38;5;12m  - Fengzhao Shi, Yanan Cao, Yanmin Shang, Yuchen Zhou, Chuan Zhou, Jia Wu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/10.1145/3485447.3512195)[39m

[38;5;12m- [39m[38;5;14m[1mActive Learning for Human-in-the-loop Customs Inspection (TKDE 2022)[0m
[38;5;12m  - Sundong Kim, Tung-Duong Mai, Thi Nguyen Duc Khanh, Sungwon Han, Sungwon Park, Karandeep Singh, Meeyoung Cha[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/9695316/)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/Seondong/Customs-Fraud-Detection)[39m

[38;5;12m- [39m[38;5;14m[1mKnowledge Sharing via Domain Adaptation in Customs Fraud Detection (AAAI 2022)[0m
[38;5;12m  - Sungwon Park, Sundong Kim, Meeyoung Cha[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2201.06759)[39m

[38;2;255;187;0m[4m2021[0m
[38;5;12m- [39m[38;5;14m[1mTowards Consumer Loan Fraud Detection: Graph Neural Networks with Role-Constrained Conditional Random Field (AAAI 2021)[0m
[38;5;12m  - Bingbing Xu, Huawei Shen, Bing-Jie Sun, Rong An, Qi Cao, Xueqi Cheng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ojs.aaai.org/index.php/AAAI/article/view/16582)[39m

[38;5;12m- [39m[38;5;14m[1mModeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection (AAAI 2021)[0m
[38;5;12m  - Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, Qing He[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2008.05600)[39m

[38;5;12m- [39m[38;5;14m[1mIFDDS: An Anti-fraud Outbound Robot (AAAI 2021)[0m
[38;5;12m  - Zihao Wang, Minghui Yang, Chunxiang Jin, Jia Liu, Zujie Wen, Saishuai Liu, Zhe Zhang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ojs.aaai.org/index.php/AAAI/article/view/18030)[39m

[38;5;12m- [39m[38;5;14m[1mModeling Heterogeneous Graph Network on Fraud Detection: A Community-based Framework with Attention Mechanism (CIKM 2021)[0m
[38;5;12m  - Li Wang, Peipei Li, Kai Xiong, Jiashu Zhao, Rui Lin[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3459637.3482277)[39m

[38;5;12m- [39m[38;5;14m[1mFraud Detection under Multi-Sourced Extremely Noisy Annotations (CIKM 2021)[0m
[38;5;12m  - Chuang Zhang, Qizhou Wang, Tengfei Liu, Xun Lu, Jin Hong, Bo Han, Chen Gong[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://gcatnjust.github.io/ChenGong/paper/zhang_cikm21.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mAdversarial Reprogramming of Pretrained Neural Networks for Fraud Detection (CIKM 2021)[0m
[38;5;12m  - Lingwei Chen, Yujie Fan, Yanfang Ye[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3459637.3482053)[39m

[38;5;12m- [39m[38;5;14m[1mFine-Grained Element Identification in Complaint Text of Internet Fraud (CIKM 2021)[0m
[38;5;12m  - Tong Liu, Siyuan Wang, Jingchao Fu, Lei Chen, Zhongyu Wei, Yaqi Liu, Heng Ye, Liaosa Xu, Weiqiang Wang, Xuanjing Huang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2108.08676)[39m

[38;5;12m- [39m[38;5;14m[1mCould You Describe the Reason for the Transfer: A Reinforcement Learning Based Voice-Enabled Bot Protecting Customers from Financial Frauds (CIKM 2021)[0m
[38;5;12m  - Zihao Wang, Fudong Wang, Haipeng Zhang, Minghui Yang, Shaosheng Cao, Zujie Wen, Zhe Zhang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3459637.3481906)[39m

[38;5;12m- [39m[38;5;14m[1mOnline Credit Payment Fraud Detection via Structure-Aware Hierarchical Recurrent Neural Network (IJCAI 2021)[0m
[38;5;12m  - Wangli Lin, Li Sun, Qiwei Zhong, Can Liu, Jinghua Feng, Xiang Ao, Hao Yang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2021/505)[39m

[38;5;12m- [39m[38;5;14m[1mIntention-aware Heterogeneous Graph Attention Networks for Fraud Transactions Detection (KDD 2021)[0m
[38;5;12m  - Can Liu, Li Sun, Xiang Ao, Jinghua Feng, Qing He, Hao Yang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/10.1145/3447548.3467142)[39m

[38;5;12m- [39m[38;5;14m[1mLive-Streaming Fraud Detection: A Heterogeneous Graph Neural Network Approach (KDD 2021)[0m
[38;5;12m  - Haishuai Wang, Zhao Li, Peng Zhang, Jiaming Huang, Pengrui Hui, Jian Liao, Ji Zhang, Jiajun Bu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3447548.3467065)[39m

[38;5;12m- [39m[38;5;14m[1mCustoms Fraud Detection in the Presence of Concept Drift (IncrLearn@ICDM 2021)[0m
[38;5;12m  - Tung-Duong Mai, Kien Hoang, Aitolkyn Baigutanova, Gaukhartas Alina, Sundong Kim[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2109.14155)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mPick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection (WWW 2021)[0m
[38;5;12m  - Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3442381.3449989)[39m

[38;2;255;187;0m[4m2020[0m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mSpatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection (AAAI 2020)[0m
[38;5;12m  - Dawei Cheng, Sheng Xiang, Chencheng Shang, Yiyi Zhang, Fangzhou Yang, Liqing Zhang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/Papers/AAAI/2020GB/AISI-ChengD.87.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mFlowScope: Spotting Money Laundering Based on Graphs (AAAI 2020)[0m
[38;5;12m  - Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi, Bryan Hooi, He Huang, Xueqi Cheng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://shenghua-liu.github.io/papers/aaai2020cr-flowscope.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/aplaceof/FlowScope)[39m

[38;5;12m- [39m[38;5;14m[1mEnhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters (CIKM 2020)[0m
[38;5;12m  - Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, Philip S. Yu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2008.08692)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/YingtongDou/CARE-GNN)[39m

[38;5;12m- [39m[38;5;14m[1mLoan Default Analysis with Multiplex Graph Learning (CIKM 2020)[0m
[38;5;12m  - Binbin Hu, Zhiqiang Zhang, Jun Zhou, Jingli Fang, Quanhui Jia, Yanming Fang, Quan Yu, Yuan Qi[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/343626706_Loan_Default_Analysis_with_Multiplex_Graph_Learning)[39m

[38;5;12m- [39m[38;5;14m[1mError-Bounded Graph Anomaly Loss for GNNs (CIKM 2020)[0m
[38;5;12m  - Tong Zhao, Chuchen Deng, Kaifeng Yu, Tianwen Jiang, Daheng Wang, Meng Jiang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.meng-jiang.com/pubs/gal-cikm20/gal-cikm20-paper.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/zhao-tong/Graph-Anomaly-Loss)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mBotSpot: A Hybrid Learning Framework to Uncover Bot Install Fraud in Mobile Advertising (CIKM 2020)[0m
[38;5;12m  - Tianjun Yao, Qing Li, Shangsong Liang, Yadong Zhu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/pdf/10.1145/3340531.3412690)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/akakeigo2020/CIKM-Applied_Research-2150)[39m

[38;5;12m- [39m[38;5;14m[1mEarly Fraud Detection with Augmented Graph Learning (DLG@KDD 2020)[0m
[38;5;12m  - Tong Zhao, Bo Ni, Wenhao Yu, Meng Jiang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.meng-jiang.com/pubs/earlyfraud-dlg20/earlyfraud-dlg20-paper.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mNAG: Neural Feature Aggregation Framework for Credit Card Fraud Detection (ICDM 2020)[0m
[38;5;12m  - Kanishka Ghosh Dastidar, Johannes Jurgovsky, Wissam Siblini, Liyun He-Guelton, Michael Granitzer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.computer.org/csdl/proceedings-article/icdm/2020/831600a092/1r54A3Sb2yk)[39m

[38;5;12m- [39m[38;5;14m[1mHeterogeneous Mini-Graph Neural Network and Its Application to Fraud Invitation Detection (ICDM 2020)[0m
[38;5;12m  - Yong-Nan Zhu, Xiaotian Luo, Yu-Feng Li, Bin Bu, Kaibo Zhou, Wenbin Zhang, Mingfan Lu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://cs.nju.edu.cn/liyf/paper/icdm20-hmgnn.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mCollaboration Based Multi-Label Propagation for Fraud Detection (IJCAI 2020)[0m
[38;5;12m  - Haobo Wang, Zhao Li, Jiaming Huang, Pengrui Hui, Weiwei Liu, Tianlei Hu, Gang Chen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/2020/343)[39m

[38;5;12m- [39m[38;5;14m[1mThe Behavioral Sign of Account Theft: Realizing Online Payment Fraud Alert (IJCAI 2020)[0m
[38;5;12m  - Cheng Wang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/2020/0636.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mFederated Meta-Learning for Fraudulent Credit Card Detection (IJCAI 2020)[0m
[38;5;12m  - Wenbo Zheng, Lan Yan, Chao Gou, Fei-Yue Wang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/2020/642)[39m

[38;5;12m- [39m[38;5;14m[1mRobust Spammer Detection by Nash Reinforcement Learning (KDD 2020)[0m
[38;5;12m  - Yingtong Dou, Guixiang Ma, Philip S. Yu, Sihong Xie[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2006.06069)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/YingtongDou/Nash-Detect)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mDATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection (KDD 2020)[0m
[38;5;12m  - Sundong Kim, Yu-Che Tsai, Karandeep Singh, Yeonsoo Choi, Etim Ibok, Cheng-Te Li, Meeyoung Cha[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://seondong.github.io/assets/papers/2020_KDD_DATE.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/Roytsai27/Dual-Attentive-Tree-aware-Embedding)[39m

[38;5;12m- [39m[38;5;14m[1mFraud Transactions Detection via Behavior Tree with Local Intention Calibration (KDD 2020)[0m
[38;5;12m  - Can Liu, Qiwei Zhong, Xiang Ao, Li Sun, Wangli Lin, Jinghua Feng, Qing He, Jiayu Tang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/pdf/10.1145/3394486.3403354)[39m

[38;5;12m- [39m[38;5;14m[1mInterleaved Sequence RNNs for Fraud Detection (KDD 2020)[0m
[38;5;12m  - Bernardo Branco, Pedro Abreu, Ana Sofia Gomes, Mariana S. C. Almeida, João Tiago Ascensão, Pedro Bizarro[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2002.05988)[39m

[38;5;12m- [39m[38;5;14m[1mGCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection (SIGIR 2020)[0m
[38;5;12m  - Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, Lizhen Cui[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2005.10150)[39m
[38;5;12m [39m
[38;5;12m- [39m[38;5;14m[1mAlleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection (SIGIR 2020)[0m
[38;5;12m  - Zhiwei Liu, Yingtong Dou, Philip S. Yu, Yutong Deng, Hao Peng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2005.00625)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/DGFraud)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mFriend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks (WWW 2020)[0m
[38;5;12m  - Adam Breuer, Roee Eilat, Udi Weinsberg[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2004.04834)[39m

[38;5;12m- [39m[38;5;14m[1mFinancial Defaulter Detection on Online Credit Payment via Multi-view Attributed Heterogeneous Information Network (WWW 2020)[0m
[38;5;12m  - Qiwei Zhong, Yang Liu, Xiang Ao, Binbin Hu, Jinghua Feng, Jiayu Tang, Qing He[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3366423.3380159)[39m

[38;5;12m- [39m[38;5;14m[1mASA: Adversary Situation Awareness via Heterogeneous Graph Convolutional Networks (WWW 2020)[0m
[38;5;12m  - Rui Wen, Jianyu Wang, Chunming Wu, Jian Xiong[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/10.1145/3366424.3391266)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mModeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection (WWW 2020)[0m
[38;5;12m  - Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Xi Gu, Qing He[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/fullHtml/10.1145/3366423.3380172)[39m
[38;5;12m  [39m
[38;2;255;187;0m[4m2019[0m
[38;5;12m- [39m[38;5;14m[1mSliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs (DSAA 2019)[0m
[38;5;12m  - Hamed Nilforoshan, Neil Shah[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1908.07087)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/hamedn/SliceNDice)[39m

[38;5;12m- [39m[38;5;14m[1mFARE: Schema-Agnostic Anomaly Detection in Social Event Logs (DSAA 2019)[0m
[38;5;12m  - Neil Shah[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://nshah.net/publications/FARE.DSAA.19.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mCash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism (AAAI 2019)[0m
[38;5;12m  - Binbin Hu, Zhiqiang Zhang, Chuan Shi, Jun Zhou, Xiaolong Li, Yuan Qi[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ojs/index.php/AAAI/article/view/3884)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/DGFraud)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mGeniePath: Graph Neural Networks with Adaptive Receptive Paths (AAAI 2019)[0m
[38;5;12m  - Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1802.00910)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/DGFraud)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mSAFE: A Neural Survival Analysis Model for Fraud Early Detection (AAAI 2019)[0m
[38;5;12m  - Panpan Zheng, Shuhan Yuan, Xintao Wu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1809.04683v2)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/PanpanZheng/SAFE)[39m

[38;5;12m- [39m[38;5;14m[1mOne-Class Adversarial Nets for Fraud Detection (AAAI 2019)[0m
[38;5;12m  - Panpan Zheng, Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1803.01798)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/ILoveAI2019/OCAN)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mUncovering Download Fraud Activities in Mobile App Markets (ASONAM 2019)[0m
[38;5;12m  - Yingtong Dou, Weijian Li, Zhirong Liu, Zhenhua Dong, Jiebo Luo, Philip S. Yu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1907.03048.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mSpam Review Detection with Graph Convolutional Networks (CIKM 2019)[0m
[38;5;12m  - Ao Li, Zhou Qin, Runshi Liu, Yiqun Yang, Dong Li[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1908.10679)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/DGFraud)[39m

[38;5;12m- [39m[38;5;14m[1mKey Player Identification in Underground Forums Over Attributed Heterogeneous Information Network Embedding Framework (CIKM 2019)[0m
[38;5;12m   - Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, Chuan Shi[39m
[38;5;12m   - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://mason.gmu.edu/~lzhao9/materials/papers/lp0110-zhangA.pdf)[39m
[38;5;12m   - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/DGFraud)[39m
[38;5;12m   [39m
[38;5;12m- [39m[38;5;14m[1mCatchCore: Catching Hierarchical Dense Subtensor (ECML-PKDD 2019)[0m
[38;5;12m  -  Wenjie Feng, Shenghua Liu, Huawei Shen, and Xueqi Cheng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://shenghua-liu.github.io/papers/pkdd2019-catchcore.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/wenchieh/catchcore)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mSpotting Collective Behaviour of Online Frauds in Customer Reviews (IJCAI 2019)[0m
[38;5;12m  - Sarthika Dhawan, Siva Charan Reddy Gangireddy, Shiv Kumar, Tanmoy Chakraborty[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1905.13649)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/LCS2-IIITD/DeFrauder)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mA Semi-Supervised Graph Attentive Network for Fraud Detection (ICDM 2019)[0m
[38;5;12m  - Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, and Qi Yuan [39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2003.01171)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/DGFraud)[39m
[38;5;12m [39m
[38;5;12m- [39m[38;5;14m[1mEigenPulse: Detecting Surges in Large Streaming Graphs with Row Augmentation (PAKDD 2019)[0m
[38;5;12m  - Jiabao Zhang, Shenghua Liu, Wenjian Yu, Wenjie Feng, Xueqi Cheng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://shenghua-liu.github.io/papers/pakdd2019-eigenpulse.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mUncovering Insurance Fraud Conspiracy with Network Learning (SIGIR 2019)[0m
[38;5;12m  - Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang, Yuan Qi[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=3331372)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mA Contrast Metric for Fraud Detection in Rich Graphs (TKDE 2019)[0m
[38;5;12m  - Shenghua Liu, Bryan Hooi, Christos Faloutsos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://shenghua-liu.github.io/papers/tkde2019-constrastsusp_holoscope.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mThink Outside the Dataset: Finding Fraudulent Reviews using Cross-Dataset Analysis (WWW 2019)[0m
[38;5;12m  - Shirin Nilizadeh, Hojjat Aghakhani, Eric Gustafson, Christopher Kruegel, Giovanni Vigna[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/333060486_Think_Outside_the_Dataset_Finding_Fraudulent_Reviews_using_Cross-Dataset_Analysis)[39m

[38;5;12m- [39m[38;5;14m[1mSecuring the Deep Fraud Detector in Large-Scale E-Commerce Platform via Adversarial Machine Learning Approach (WWW 2019)[0m
[38;5;12m  - Qingyu Guo, Zhao Li, Bo An, Pengrui Hui, Jiaming Huang, Long Zhang, Mengchen Zhao[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ntu.edu.sg/home/boan/papers/WWW19.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mNo Place to Hide: Catching Fraudulent Entities in Tensors (WWW 2019)[0m
[38;5;12m  - Yikun Ban, Xin Liu, Ling Huang, Yitao Duan, Xue Liu, Wei Xu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1810.06230.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mFdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review System (WWW 2019)[0m
[38;5;12m  - Rui Wen, Jianyu Wang and Yu Huang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=3316586)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/DGFraud)[39m

[38;2;255;187;0m[4m2018[0m

[38;5;12m- [39m[38;5;14m[1mHeterogeneous Graph Neural Networks for Malicious Account Detection (CIKM 2018)[0m
[38;5;12m  - Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/10.1145/3269206.3272010)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/DGFraud)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mReinforcement Mechanism Design for Fraudulent Behaviour in e-Commerce (AAAI 2018)[0m
[38;5;12m  - Qingpeng Cai, Aris Filos-Ratsikas, Pingzhong Tang, Yiwei Zhang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16650)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mAdapting to Concept Drift in Credit Card Transaction Data Streams Using Contextual Bandits and Decision Trees (AAAI 2018)[0m
[38;5;12m  - Dennis J. N. J. Soemers, Tim Brys, Kurt Driessens, Mark H. M. Winands, Ann Nowé[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16183/16394)[39m

[38;5;12m- [39m[38;5;14m[1mNextgen AML: Distributed Deep Learning Based Language Technologies to Augment Anti Money Laundering Investigation(ACL 2018)[0m
[38;5;12m  - Jingguang Han, Utsab Barman, Jeremiah Hayes, Jinhua Du, Edward Burgin, Dadong Wan[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aclweb.org/anthology/P18-4007)[39m

[38;5;12m- [39m[38;5;14m[1mPreserving Privacy of Fraud Detection Rule Sharing Using Intel's SGX (CIKM 2018)[0m
[38;5;12m  - Daniel Deutch, Yehonatan Ginzberg, Tova Milo[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/328439345_Preserving_Privacy_of_Fraud_Detection_Rule_Sharing_Using_Intel%27s_SGX)[39m

[38;5;12m- [39m[38;5;14m[1mDeep Structure Learning for Fraud Detection (ICDM 2018)[0m
[38;5;12m  - Haibo Wang, Chuan Zhou, Jia Wu, Weizhen Dang, Xingquan Zhu, Jilong Wang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/330030140_Deep_Structure_Learning_for_Fraud_Detection)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Sequential Behavior Representations for Fraud Detection (ICDM 2018)[0m
[38;5;12m  - Jia Guo, Guannan Liu, Yuan Zuo, Junjie Wu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/330028902_Learning_Sequential_Behavior_Representations_for_Fraud_Detection)[39m

[38;5;12m- [39m[38;5;14m[1mImpression Allocation for Combating Fraud in E-commerce Via Deep Reinforcement Learning with Action Norm Penalty (IJCAI 2018)[0m
[38;5;12m  - Mengchen Zhao, Zhao Li, Bo An, Haifeng Lu, Yifan Yang, Chen Chu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2018/0548.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mTax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach (KDD 2018)[0m
[38;5;12m  - Daniel de Roux, Boris Perez, Andrés Moreno, María-Del-Pilar Villamil, César Figueroa[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.kdd.org/kdd2018/accepted-papers/view/tax-fraud-detection-for-under-reporting-declarations-using-an-unsupervised-)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mCollective Fraud Detection Capturing Inter-Transaction Dependency (KDD 2018)[0m
[38;5;12m  - Bokai Cao, Mia Mao, Siim Viidu, Philip Yu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v71/cao18a.html)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mFraud Detection with Density Estimation Trees (KDD 2018)[0m
[38;5;12m  - Fraud Detection with Density Estimation Trees[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v71/ram18a/ram18a.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mReal-time Constrained Cycle Detection in Large Dynamic Graphs (VLDB 2018)[0m
[38;5;12m  - Xiafei Qiu, Wubin Cen, Zhengping Qian, You Peng, Ying Zhang, Xuemin Lin, Jingren Zhou[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.vldb.org/pvldb/vol11/p1876-qiu.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mREV2: Fraudulent User Prediction in Rating Platforms (WSDM 2018)[0m
[38;5;12m  - Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, V. S. Subrahmanian[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://cs.stanford.edu/~srijan/pubs/rev2-wsdm18.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://cs.stanford.edu/~srijan/rev2/)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mExposing Search and Advertisement Abuse Tactics and Infrastructure of Technical Support Scammers (WWW 2018)[0m
[38;5;12m  - Bharat Srinivasan, Athanasios Kountouras, Najmeh Miramirkhani, Monjur Alam, Nick Nikiforakis, Manos Antonakakis, Mustaque Ahamad[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.securitee.org/files/tss_www2018.pdf)[39m

[38;2;255;187;0m[4m2017[0m
[38;5;12m- [39m[38;5;14m[1mZooBP: Belief Propagation for Heterogeneous Networks (VLDB 2017)[0m
[38;5;12m  - Dhivya Eswaran, Stephan Gunnemann, Christos Faloutsos, Disha Makhija, Mohit Kumar[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.vldb.org/pvldb/vol10/p625-eswaran.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/UGFraud)[39m

[38;5;12m- [39m[38;5;14m[1mBehavioral Analysis of Review Fraud: Linking Malicious Crowdsourcing to Amazon and Beyond (AAAI 2017)[0m
[38;5;12m  - Parisa Kaghazgaran, James Caverlee, Majid Alfifi[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15659)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mDetection of Money Laundering Groups: Supervised Learning on Small Networks (AAAI 2017)[0m
[38;5;12m  - David Savage, Qingmai Wang, Xiuzhen Zhang, Pauline Chou, Xinghuo Yu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1608.00708.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mSpectrum-based Deep Neural Networks for Fraud Detection (CIKM 2017)[0m
[38;5;12m  - Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1706.00891)[39m

[38;5;12m- [39m[38;5;14m[1mHoloScope: Topology-and-Spike Aware Fraud Detection (CIKM 2017)[0m
[38;5;12m  - Shenghua Liu, Bryan Hooi, Christos Faloutsos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1705.02505)[39m

[38;5;12m- [39m[38;5;14m[1mThe Many Faces of Link Fraud (ICDM 2017)[0m
[38;5;12m  - Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1704.01420)[39m

[38;5;12m- [39m[38;5;14m[1mHitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks (ICDM 2017)[0m
[38;5;12m  - Bokai Cao, Mia Mao, Siim Viidu, Philip S. Yu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1709.04129)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mGANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs (ICDM 2017)[0m
[38;5;12m  - Binghui Wang, Neil Zhenqiang Gong, Hao Fu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/8215519)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/UGFraud)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mImproving Card Fraud Detection Through Suspicious Pattern Discovery (IEA/AIE 2017)[0m
[38;5;12m  - Fabian Braun, Olivier Caelen, Evgueni N. Smirnov, Steven Kelk, Bertrand Lebichot:[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.oliviercaelen.be/doc/GBSSCCFDS.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mOnline Reputation Fraud Campaign Detection in User Ratings (IJCAI 2017)[0m
[38;5;12m  - Chang Xu, Jie Zhang, Zhu Sun[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2017/0541.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mUncovering Unknown Unknowns in Financial Services Big Data by Unsupervised Methodologies: Present and Future trends (KDD 2017)[0m
[38;5;12m  - Gil Shabat, David Segev, Amir Averbuch[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v71/shabat18a.html)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mPD-FDS: Purchase Density based Online Credit Card Fraud Detection System (KDD 2017)[0m
[38;5;12m  - Youngjoon Ki, Ji Won Yoon [39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v71/ki18a/ki18a.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mHiDDen: Hierarchical Dense Subgraph Detection with Application to Financial Fraud Detection (SDM 2017)[0m
[38;5;12m  - Si Zhang, Dawei Zhou, Mehmet Yigit Yildirim, Scott Alcorn, Jingrui He, Hasan Davulcu, Hanghang Tong[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.public.asu.edu/~hdavulcu/SDM17.pdf)[39m

[38;2;255;187;0m[4m2016[0m
[38;5;12m- [39m[38;5;14m[1mA Fraud Resilient Medical Insurance Claim System (AAAI 2016)[0m
[38;5;12m  - Yuliang Shi, Chenfei Sun, Qingzhong Li, Lizhen Cui, Han Yu, Chunyan Miao[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11813)[39m

[38;5;12m- [39m[38;5;14m[1mA Graph-Based, Semi-Supervised, Credit Card Fraud Detection System (COMPLEX NETWORKS 2016)[0m
[38;5;12m  - Bertrand Lebichot, Fabian Braun, Olivier Caelen, Marco Saerens[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.oliviercaelen.be/doc/IEAAIE_2017_Finalversion-PDF_39.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mFRAUDAR: Bounding Graph Fraud in the Face of Camouflage (KDD 2016)[0m
[38;5;12m  - Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.andrew.cmu.edu/user/bhooi/papers/fraudar_kdd16.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/UGFraud)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mIdentifying Anomalies in Graph Streams Using Change Detection (KDD 2016)[0m
[38;5;12m  - William Eberle and Lawrence Holde[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.mlgworkshop.org/2016/paper/MLG2016_paper_12.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mFairPlay: Fraud and Malware Detection in Google Play (SDM 2016)[0m
[38;5;12m  - Mahmudur Rahman, Mizanur Rahman, Bogdan Carbunar, Duen Horng Chau[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1703.02002)[39m

[38;5;12m- [39m[38;5;14m[1mBIRDNEST: Bayesian Inference for Ratings-Fraud Detection (SDM 2016)[0m
[38;5;12m  - Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.andrew.cmu.edu/user/bhooi/papers/birdnest_sdm16.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mUnderstanding the Detection of View Fraud in Video Content Portals (WWW 2016)[0m
[38;5;12m  - Miriam Marciel, Rubén Cuevas, Albert Banchs, Roberto Gonzalez, Stefano Traverso, Mohamed Ahmed, Arturo Azcorra[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=2882980)[39m

[38;2;255;187;0m[4m2015[0m
[38;5;12m- [39m[38;5;14m[1mToward An Intelligent Agent for Fraud Detection — The CFE Agent (AAAI 2015)[0m
[38;5;12m  - Joe Johnson[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/ocs/index.php/FSS/FSS15/paper/download/11664/11485)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mGraph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data (AAAI 2015)[0m
[38;5;12m  - Juan Liu, Eric Bier, Aaron Wilson, Tomonori Honda, Kumar Sricharan, Leilani Gilpin, John Alexis Guerra Gómez, Daniel Davies[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/1ea7/125b789ef938bffe10c7588e6b071c4ff73c.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mRobust System for Identifying Procurement Fraud (AAAI 2015)[0m
[38;5;12m  - Amit Dhurandhar, Rajesh Kumar Ravi, Bruce Graves, Gopikrishnan Maniachari, Markus Ettl[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/27af/c9ec453ae0cf9e55f4032ff688cb70c2a61e.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mFraud Transaction Recognition: A Money Flow Network Approach (CIKM 2015)[0m
[38;5;12m  - Renxin Mao, Zhao Li, Jinhua Fu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=2806647)[39m

[38;5;12m- [39m[38;5;14m[1mTowards Collusive Fraud Detection in Online Reviews (ICDM 2015)[0m
[38;5;12m  - Chang Xu, Jie Zhang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/7373434)[39m

[38;5;12m- [39m[38;5;14m[1mCatch the Black Sheep: Unified Framework for Shilling Attack Detection Based on Fraudulent Action Propagation (IJCAI 2015)[0m
[38;5;12m  - Yongfeng Zhang, Yunzhi Tan, Min Zhang, Yiqun Liu, Tat-Seng Chua, Shaoping Ma[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/15/Papers/341.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mCollective Opinion Spam Detection: Bridging Review Networks and Metadata (KDD 2015)[0m
[38;5;12m  - Shebuti Rayana, Leman Akoglu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.andrew.cmu.edu/user/lakoglu/pubs/15-kdd-collectiveopinionspam.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/UGFraud)[39m

[38;5;12m- [39m[38;5;14m[1mGraph-Based User Behavior Modeling: From Prediction to Fraud Detection (KDD 2015)[0m
[38;5;12m  - Alex Beutel, Leman Akoglu, Christos Faloutsos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cs.cmu.edu/~abeutel/kdd2015_tutorial/tutorial.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mFrauDetector: A Graph-Mining-based Framework for Fraudulent Phone Call Detection (KDD 2015)[0m
[38;5;12m  - Vincent S. Tseng, Jia-Ching Ying, Che-Wei Huang, Yimin Kao, Kuan-Ta Chen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://repository.ncku.edu.tw/bitstream/987654321/166322/1/4010204000-000004_1.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mA Framework for Intrusion Detection Based on Frequent Subgraph Mining (SDM 2015)[0m
[38;5;12m  - Vitali Herrera-Semenets, Niusvel Acosta-Mendoza, Andres Gago-Alonso[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/271839253_A_Framework_for_Intrusion_Detection_based_on_Frequent_Subgraph_Mining)[39m

[38;5;12m- [39m[38;5;14m[1mCrowd Fraud Detection in Internet Advertising (WWW 2015)[0m
[38;5;12m  - Tian Tian, Jun Zhu, Fen Xia, Xin Zhuang, Tong Zhang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.www2015.it/documents/proceedings/proceedings/p1100.pdf)[39m

[38;2;255;187;0m[4m2014[0m
[38;5;12m- [39m[38;5;14m[1mSpotting Suspicious Link Behavior with fBox: An Adversarial Perspective (ICDM 2014)[0m
[38;5;12m  - Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1410.3915.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/safe-graph/UGFraud)[39m

[38;5;12m- [39m[38;5;14m[1mFraudulent Support Telephone Number Identification Based on Co-Occurrence Information on the Web (AAAI 2014)[0m
[38;5;12m  - Xin Li, Yiqun Liu, Min Zhang, Shaoping Ma[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/2733/1f48c87736ea12b9edec062e384d3bd58f88.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mCorporate Residence Fraud Detection (KDD 2014)[0m
[38;5;12m  - Enric Junqué de Fortuny, Marija Stankova, Julie Moeyersoms, Bart Minnaert, Foster J. Provost, David Martens[39m
[38;5;12m  [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m [39m
[38;5;12m(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_f1a6f763ef7088a4fb4b1[39m
[38;5;12ma4ff94856f8)[39m

[38;5;12m- [39m[38;5;14m[1mGraphical Models for Identifying Fraud and Waste in Healthcare Claims (SDM 2014)[0m
[38;5;12m  - Peder A. Olsen, Ramesh Natarajan, Sholom M. Weiss[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://epubs.siam.org/doi/abs/10.1137/1.9781611973440.66)[39m

[38;5;12m- [39m[38;5;14m[1mImproving Credit Card Fraud Detection with Calibrated Probabilities (SDM 2014)[0m
[38;5;12m  - Alejandro Correa Bahnsen, Aleksandar Stojanovic, Djamila Aouada, Björn E. Ottersten[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/9241/ef2a2f6638eafeffd0056736c0f46f9aa083.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mLarge Graph Mining: Patterns, Cascades, Fraud Detection, and Algorithms (WWW 2014)[0m
[38;5;12m  - Christos Faloutsos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://wwwconference.org/proceedings/www2014/proceedings/p1.pdf)[39m
[38;5;12m  [39m
[38;2;255;187;0m[4m2013[0m

[38;5;12m- [39m[38;5;14m[1mOpinion Fraud Detection in Online Reviews by Network Effects (AAAI 2013)[0m
[38;5;12m  - Leman Akoglu, Rishi Chandy, Christos Faloutsos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/279905898_Opinion_fraud_detection_in_online_reviews_by_network_effects)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mUsing Social Network Knowledge for Detecting Spider Constructions in Social Security Fraud (ASONAM 2013)[0m
[38;5;12m  - Véronique Van Vlasselaer, Jan Meskens, Dries Van Dromme, Bart Baesens [39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/6785796)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mRanking Fraud Detection for Mobile Apps: a Holistic View (CIKM 2013)[0m
[38;5;12m  - Hengshu Zhu, Hui Xiong, Yong Ge, Enhong Chen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://dm.ustc.edu.cn/zhu-cikm13.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mUsing Co-Visitation Networks for Detecting Large Scale Online Display Advertising Exchange Fraud (KDD 2013)[0m
[38;5;12m  - Ori Stitelman, Claudia Perlich, Brian Dalessandro, Rod Hook, Troy Raeder, Foster J. Provost[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://chbrown.github.io/kdd-2013-usb/kdd/p1240.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mAdaptive Adversaries: Building Systems to Fight Fraud and Cyber Intruders (KDD 2013)[0m
[38;5;12m  - Ari Gesher[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=2491134)[39m

[38;5;12m- [39m[38;5;14m[1mAnomaly, Event, and Fraud Detection in Large Network Datasets (WSDM 2013)[0m
[38;5;12m  - Leman Akoglu, Christos Faloutsos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.andrew.cmu.edu/user/lakoglu/wsdm13/13-wsdm-tutorial.pdf)[39m

[38;2;255;187;0m[4m2012[0m

[38;5;12m- [39m[38;5;14m[1mFraud Detection: Methods of Analysis for Hypergraph Data (ASONAM 2012)[0m
[38;5;12m  - Anna Leontjeva, Konstantin Tretyakov, Jaak Vilo, and Taavi Tamkivi[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6425618)[39m

[38;5;12m- [39m[38;5;14m[1mOnline Modeling of Proactive Moderation System for Auction Fraud Detection (WWW 2012)[0m
[38;5;12m  - Liang Zhang, Jie Yang, Belle L. Tseng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.chennaisunday.com/Java%202012%20Base%20Paper/Online%20Modeling%20of%20Proactive%20Moderation%20System%20for%20Auction%20Fraud%20Detection.pdf)[39m

[38;2;255;187;0m[4m2011[0m
[38;5;12m- [39m[38;5;14m[1mA Machine-Learned Proactive Moderation System for Auction Fraud Detection (CIKM 2011)[0m
[38;5;12m  - Liang Zhang, Jie Yang, Wei Chu, Belle L. Tseng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.gatsby.ucl.ac.uk/~chuwei/paper/p2501-zhang.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mA Taxi Driving Fraud Detection System (ICDM 2011)[0m
[38;5;12m  - Yong Ge, Hui Xiong, Chuanren Liu, Zhi-Hua Zhou[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/6137222)[39m

[38;5;12m- [39m[38;5;14m[1mUtility-Based Fraud Detection (IJCAI 2011)[0m
[38;5;12m  - Luís Torgo, Elsa Lopes[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/11/Papers/255.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mA Pattern Discovery Approach to Retail Fraud Detection (KDD 2011)[0m
[38;5;12m  - Prasad Gabbur, Sharath Pankanti, Quanfu Fan, Hoang Trinh[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www2.engr.arizona.edu/~pgsangam/gabbur_kdd_11.pdf)[39m

[38;2;255;187;0m[4m2010[0m

[38;5;12m- [39m[38;5;14m[1mHunting for the Black Swan: Risk Mining from Text (ACL 2010)[0m
[38;5;12m  - JL Leidner, F Schilder[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aclweb.org/anthology/P10-4010)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mFraud Detection by Generating Positive Samples for Classification from Unlabeled Data (ACL 2010)[0m
[38;5;12m  - Levente Kocsis, Andras George[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.szit.bme.hu/~gya/publications/KocsisGyorgy.pdf)[39m
[38;5;12m  [39m
[38;2;255;187;0m[4m2009[0m
[38;5;12m- [39m[38;5;14m[1mSVM-based Credit Card Fraud Detection with Reject Cost and Class-Dependent Error Cost (PAKDD 2009)[0m
[38;5;12m  - En-hui Zheng,Chao Zou,Jian Sun, Le Chen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.semanticscholar.org/paper/SVM-Based-Cost-sensitive-Classification-Algorithm-Zheng-Zou/bcae06626ccd453925ef040a1edb5cbb10b862ef)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mAn Approach for Automatic Fraud Detection in the Insurance Domain (AAAI 2009)[0m
[38;5;12m  - Alexander Widder, Rainer v. Ammon, Gerit Hagemann, Dirk Schönfeld[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.325.3231&rep=rep1&type=pdf)[39m
[38;5;12m  [39m
[38;2;255;187;0m[4m2007[0m
[38;5;12m- [39m[38;5;14m[1mRelational Data Pre-Processing Techniques for Improved Securities Fraud Detection (KDD 2007)[0m
[38;5;12m  - Andrew S. Fast, Lisa Friedland, Marc E. Maier, Brian J. Taylor, David D. Jensen, Henry G. Goldberg, John Komoroske[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=1281192.1281293)[39m

[38;5;12m- [39m[38;5;14m[1mUncovering Fraud in Direct Marketing Data with a Fraud Auditing Case Builder (PKDD 2007)[0m
[38;5;12m  - Fletcher Lu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/978-3-540-74976-9_56)[39m

[38;5;12m- [39m[38;5;14m[1mNetprobe: A Fast and Scalable System for Fraud Detection in Online Auction Networks (WWW 2007)[0m
[38;5;12m  - Shashank Pandit, Duen Horng Chau, Samuel Wang, Christos Faloutsos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.cmu.edu/~christos/PUBLICATIONS/netprobe-www07.pdf)[39m

[38;2;255;187;0m[4m2006[0m

[38;5;12m- [39m[38;5;14m[1mData Mining Approaches to Criminal Career Analysis (ICDM 2006)[0m
[38;5;12m  - Jeroen S. De Bruin, Tim K. Cocx, Walter A. Kosters, Jeroen F. J. Laros, Joost N. Kok [39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/4053045)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mLarge Scale Detection of Irregularities in Accounting Data (ICDM 2006)[0m
[38;5;12m  - Stephen Bay, Krishna Kumaraswamy, Markus G. Anderle, Rohit Kumar, David M. Steier [39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/4053036)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mCamouflaged Fraud Detection in Domains with Complex Relationships (KDD 2006)[0m
[38;5;12m  - Sankar Virdhagriswaran, Gordon Dakin[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=1150532)[39m

[38;5;12m- [39m[38;5;14m[1mDetecting Fraudulent Personalities in Networks of Online Auctioneers (PKDD 2006)[0m
[38;5;12m  - Duen Horng Chau, Shashank Pandit, Christos Faloutsos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.cs.cmu.edu/~dchau/papers/auction_fraud_pkdd06.pdf)[39m

[38;2;255;187;0m[4m2005[0m

[38;5;12m- [39m[38;5;14m[1mTechnologies to Defeat Fraudulent Schemes Related to Email Requests (AAAI 2005)[0m
[38;5;12m  - Edoardo Airoldi, Bradley Malin, and Latanya Sweeney[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.aaai.org/Library/Symposia/Spring/2005/ss05-01-023.php)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mAI Technologies to Defeat Identity Theft Vulnerabilities (AAAI 2005)[0m
[38;5;12m  - Latanya Sweeney[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dataprivacylab.org/dataprivacy/projects/idangel/paper1.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mDetecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford's Law Distributions (ECML 2005)[0m
[38;5;12m  - Fletcher Lu, J. Efrim Boritz[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://faculty.uoit.ca/fletcherlu/LuECML05.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mUsing Relational Knowledge Discovery to Prevent Securities Fraud (KDD 2005)[0m
[38;5;12m  - Jennifer Neville, Özgür Simsek, David D. Jensen, John Komoroske, Kelly Palmer, Henry G. Goldberg[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cs.purdue.edu/homes/neville/papers/neville-et-al-kdd2005.pdf)[39m

[38;2;255;187;0m[4m2003[0m
[38;5;12m- [39m[38;5;14m[1mApplying Data Mining in Investigating Money Laundering Crimes (KDD 2003)[0m
[38;5;12m  - Zhongfei (Mark) Zhang, John J. Salerno, Philip S. Yu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/9124/b61d48b7e52008c7fd5fac1b7eac38474581.pdf)[39m

[38;2;255;187;0m[4m2000[0m
[38;5;12m- [39m[38;5;14m[1mDocument Classification and Visualisation to Support the Investigation of Suspected Fraud (PKDD 2000)[0m
[38;5;12m  - Johan Hagman, Domenico Perrotta, Ralf Steinberger, and Aristi de Varfis[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/9124/b61d48b7e52008c7fd5fac1b7eac38474581.pdf)[39m
[38;5;12m  [39m
[38;2;255;187;0m[4m1999[0m
[38;5;12m- [39m[38;5;14m[1mStatistical Challenges to Inductive Inference in Linked Data. (AISTATS 1999)[0m
[38;5;12m  - David Jensen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.589.1445&rep=rep1&type=pdf)[39m

[38;2;255;187;0m[4m1998[0m

[38;5;12m- [39m[38;5;14m[1mToward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection (KDD 1998)[0m
[38;5;12m  - Phillip K Chan, Salvatore J Stolfo [39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/6e19/3366945bf3bd72d5ba906e3982ac4d8ae874.pdf)[39m
[38;5;12m  [39m
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[38;5;12m  - Jaakko Hollmén, Volker Tresp[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/1505-call-based-fraud-detection-in-mobile-communication-networks-using-a-hierarchical-regime-switching-model.pdf)[39m

[38;2;255;187;0m[4m1997[0m

[38;5;12m- [39m[38;5;14m[1mDetection of Mobile Phone Fraud Using Supervised Neural Networks: A First Prototype (ICANN 1997)[0m
[38;5;12m  - Yves Moreau, Herman Verrelst, Joos Vandewalle[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/content/pdf/10.1007%2FBFb0020294.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mProspective Assessment of AI Technologies for Fraud Detection: A Case Study (AAAI 1997)[0m
[38;5;12m  - David Jensen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/0efe/8a145cc4d52e8769bb1d13142326a154624f.pdf)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mCredit Card Fraud Detection Using Meta-Learning: Issues and Initial Results (AAAI 1997)[0m
[38;5;12m  - Salvatore J. Stolfo, David W. Fan, Wenke Lee and Andreas L. Prodromidis[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/29b3/e330e0045e5da71cc1d333bed24b7a4670f8.pdf)[39m

[38;2;255;187;0m[4m1995[0m
[38;5;12m- [39m[38;5;14m[1mFraud: Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures (UAI 1995)[0m
[38;5;12m  - Kazuo J. Ezawa, Til Schuermann[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1302.4945)[39m
[38;5;12m  [39m
[38;5;238m――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――[39m

[38;5;14m[1mLicense[0m

[38;5;12m- [39m[38;5;14m[1mCC0 Universal[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-fraud-detection-papers/blob/master/LICENSE)[39m

