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Rapid and massive adoption of mobile/ online payment services has brought new challenges to the service providers as well as regulators in safeguarding the proper uses such services/ systems.
Chem2bio2rdf: a semantic framework for linking and data mining chemogenomic and systems chemical biology data
Bin Chen, Xiao Dong, Dazhi Jiao, Huijun Wang, Qian Zhu, Ying Ding, and David J Wild · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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From source to surveillance: the hidden risk in aml monitoring system optimization, 2010
PricewaterhouseCoopers · 2010
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Genetic clustering algorithms for detecting money-laundering
Llúıs Alsedà, Abhishek Awasthi, and Jörg Lässig · 2012
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Distributed large-scale natural graph factorization
Amr Ahmed, Nino Shervashidze, Shravan Narayanamurthy, Vanja Josifovski, and Alexander J Smola · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Grarep: Learning graph representations with global structural information
Shaosheng Cao, Wei Lu, and Qiongkai Xu · 2015
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Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Deep learning anomaly detection as support fraud investigation in brazilian exports and anti-money laundering
Ebberth L Paula, Marcelo Ladeira, Rommel N Carvalho, and Thiago Marzagao · 2016
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Two step credit risk assessment model for retail bank loan applications using decision tree data mining technique
M Sudhakar and CVK Reddy · 2016
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Structural deep network embedding
Daixin Wang, Peng Cui, and Wenwu Zhu · 2016
Cited alongside, same era.
Stochastic training of graph convolutional networks with variance reduction
Jianfei Chen, Jun Zhu, and Le Song · 2017
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metapath2vec: Scalable representation learning for heterogeneous networks
Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami · 2017
Cited alongside, same era.
Etherscan: The ethereum block explorer, 2017
EtherscanTeam · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
edge2vec: Learning node representation using edge semantics
Zheng Gao, Gang Fu, Chunping Ouyang, Satoshi Tsutsui, Xiaozhong Liu, and Ying Ding · 2018
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Exploiting edge features in graph neural networks, 2018
Liyu Gong and Qiang Cheng · 2018
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Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang · 2018
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Deepinf: Social influence prediction with deep learning
Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola · 2017
Cited alongside, same era.
Predicting multicellular function through multi-layer tissue networks
Marinka Zitnik and Jure Leskovec · 2017
Cited alongside, same era.
Bike flow prediction with multi-graph convolutional networks
Di Chai, Leye Wang, and Qiang Yang · 2018
Cited alongside, same era.
FastGCN: Fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
Cited alongside, same era.
From suspicion to action – converting financial intelligence into greater operational impact. financial intelligence group, 2018
Europol · 2018
Cited alongside, same era.
Scalable graph learning for anti-money laundering: A first look
Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E Leiserson, and Tao B Schardl · 2018
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Bing Yu, Haoteng Yin, and Zhanxing Zhu · 2018
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Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, and Jure Leskovec · 2018
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Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting
Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, and Yan Liu · 2019
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Deep graph library: Towards efficient and scalable deep learning on graphs
Wang Minjie, Yu Lingfan, Zheng Da, Gan Quan, Gai Yu, Ye Zihao, Li Mufei, Zhou Jinjing, Huang Qi, Ma Chao, Huang Ziyue, Guo Qipeng, Zhang Hao, Lin Haibin, Zhao Junbo, Li Jinyang, Smola Alexander, and Zhang Zheng · 2019
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Graph representation learning to prevent payment collusion fraud, 2019
Ramanathan Venkatesh · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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