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Credit card fraud poses a significant threat to the economy.
A simple model of bank bankruptcies
Agata Aleksiejuk and Janusz A Hołyst · 2001
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Credit card fraud detection using bayesian and neural networks
Sam Maes, Karl Tuyls, Bram Vanschoenwinkel, and Bernard Manderick · 2002
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Causality
Judea Pearl · 2009
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Graph echo state networks
Claudio Gallicchio and Alessio Micheli · 2010
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Data mining for credit card fraud: A comparative study
Siddhartha Bhattacharyya, Sanjeev Jha, Kurian Tharakunnel, and J Christopher Westland · 2011
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Detecting credit card fraud by decision trees and support vector machines
Yusuf G Şahin and Ekrem Duman · 2011
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From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews
Julian John McAuley and Jure Leskovec · 2013
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Collective opinion spam detection: Bridging review networks and metadata
Shebuti Rayana and Leman Akoglu · 2015
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Credit card fraud detection using convolutional neural networks
Kang Fu, Dawei Cheng, Yi Tu, and Liqing Zhang · 2016
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Suspicious behavior detection: Current trends and future directions
Meng Jiang, Peng Cui, and Christos Faloutsos · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Learning steady-states of iterative algorithms over graphs
Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, and Le Song · 2018
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Adaptive graph convolutional neural networks
Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang · 2018
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Heterogeneous graph neural networks for malicious account detection
Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song · 2018
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The book of why: the new science of cause and effect
Judea Pearl and Dana Mackenzie · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Spatial temporal graph convolutional networks for skeleton-based action recognition
Sijie Yan, Yuanjun Xiong, and Dahua Lin · 2018
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Dual graph convolutional networks for graph-based semi-supervised classification
Chenyi Zhuang and Qiang Ma · 2018
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Conceptual building of sustainable economic growth and corporate bankruptcy
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Alleviating the inconsistency problem of applying graph neural network to fraud detection
Zhiwei Liu, Yingtong Dou, Philip S Yu, Yutong Deng, and Hao Peng · 2020
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Masked label prediction: Unified message passing model for semi-supervised classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun · 2020
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Nodeaug: Semi-supervised node classification with data augmentation
Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Juncheng Liu, and Bryan Hooi · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Latifa AlFalahi and Haitham Nobanee · 2019
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
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Using generative adversarial networks for improving classification effectiveness in credit card fraud detection
Ugo Fiore, Alfredo De Santis, Francesca Perla, Paolo Zanetti, and Francesco Palmieri · 2019
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Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan · 2019
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A semi-supervised graph attentive network for financial fraud detection
Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, and Yuan Qi · 2019
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Graph wavenet for deep spatial-temporal graph modeling
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang · 2019
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Key player identification in underground forums over attributed heterogeneous information network embedding framework
Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, and Chuan Shi · 2019
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Computing graph neural networks: A survey from algorithms to accelerators
Sergi Abadal, Akshay Jain, Robert Guirado, Jorge López-Alonso, and Eduard Alarcón · 2021
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Should graph convolution trust neighbors? a simple causal inference method
Fuli Feng, Weiran Huang, Xiangnan He, Xin Xin, Qifan Wang, and Tat-Seng Chua · 2021
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Pick and choose: a gnn-based imbalanced learning approach for fraud detection
Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, and Qing He · 2021
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A machine learning based credit card fraud detection using the ga algorithm for feature selection
Emmanuel Ileberi, Yanxia Sun, and Zenghui Wang · 2022
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Mining spatio-temporal relations via self-paced graph contrastive learning
Rongfan Li, Ting Zhong, Xinke Jiang, Goce Trajcevski, Jin Wu, and Fan Zhou · 2022
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Towards robust and adaptive motion forecasting: A causal representation perspective
Yuejiang Liu, Riccardo Cadei, Jonas Schweizer, Sherwin Bahmani, and Alexandre Alahi · 2022
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Temporal and heterogeneous graph neural network for financial time series prediction
Sheng Xiang, Dawei Cheng, Chencheng Shang, Ying Zhang, and Yuqi Liang · 2022
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Uncertainty quantification via spatial-temporal tweedie model for zero-inflated and long-tail travel demand prediction
Xinke Jiang, Dingyi Zhuang, Xianghui Zhang, Hao Chen, Jiayuan Luo, and Xiaowei Gao · 2023
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Semi-supervised credit card fraud detection via attribute-driven graph representation
Sheng Xiang, Mingzhi Zhu, Dawei Cheng, Enxia Li, Ruihui Zhao, Yi Ouyang, Ling Chen, and Yefeng Zheng · 2023
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Incomplete graph learning via attribute-structure decoupled variational auto-encoder
Xinke Jiang, Zidi Qin, Jiarong Xu, and Xiang Ao · 2024
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