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Title: GitHub - safe-graph/DGFraud: A Deep Graph-based Toolbox for Fraud Detection

Open Graph Title: GitHub - safe-graph/DGFraud: A Deep Graph-based Toolbox for Fraud Detection

X Title: GitHub - safe-graph/DGFraud: A Deep Graph-based Toolbox for Fraud Detection

Description: A Deep Graph-based Toolbox for Fraud Detection. Contribute to safe-graph/DGFraud development by creating an account on GitHub.

Open Graph Description: A Deep Graph-based Toolbox for Fraud Detection. Contribute to safe-graph/DGFraud development by creating an account on GitHub.

X Description: A Deep Graph-based Toolbox for Fraud Detection. Contribute to safe-graph/DGFraud development by creating an account on GitHub.

Mail addresses
ytongdou@gmail.com
bdscsafegraph@gmail.com

Opengraph URL: https://github.com/safe-graph/DGFraud

X: @github

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PDFhttps://arxiv.org/pdf/2008.08692.pdf
PDFhttps://arxiv.org/pdf/2005.00625.pdf
PyGOD: A Python Library for Graph Outlier Detection (Anomaly Detection)https://github.com/pygod-team/pygod
UGFraud: An Unsupervised Graph-based Toolbox for Fraud Detectionhttps://github.com/safe-graph/UGFraud
Graph-based Fraud Detection Paper Listhttps://github.com/safe-graph/graph-fraud-detection-papers
Awesome Fraud Detection Papershttps://github.com/benedekrozemberczki/awesome-fraud-detection-papers
Attack and Defense Papers on Graph Datahttps://github.com/safe-graph/graph-adversarial-learning-literature
PyOD: A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)https://github.com/yzhao062/pyod
PyODD: An End-to-end Outlier Detection Systemhttps://github.com/datamllab/pyodds
DGL: Deep Graph Libraryhttps://github.com/dmlc/dgl
Outlier Detection DataSets (ODDS)http://odds.cs.stonybrook.edu/
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GraphConsishttps://arxiv.org/abs/2005.00625
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A Semi-supervised Graph Attentive Network for Financial Fraud Detectionhttps://arxiv.org/pdf/2003.01171
BibTexhttps://github.com/safe-graph/DGFraud/blob/master/reference/semignn.txt
Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Frameworkhttp://mason.gmu.edu/~lzhao9/materials/papers/lp0110-zhangA.pdf
BibTexhttps://github.com/safe-graph/DGFraud/blob/master/reference/player2vec.txt
Spam Review Detection with Graph Convolutional Networkshttps://arxiv.org/abs/1908.10679
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FdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review Systemhttps://dl.acm.org/citation.cfm?id=3316586
BibTexhttps://github.com/safe-graph/DGFraud/blob/master/reference/fdgars.txt
GeniePath: Graph Neural Networks with Adaptive Receptive Pathshttps://arxiv.org/abs/1802.00910
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Heterogeneous Graph Neural Networks for Malicious Account Detectionhttps://arxiv.org/pdf/2002.12307.pdf
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Inductive Representation Learning on Large Graphshttps://arxiv.org/pdf/1706.02216.pdf
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Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detectionhttps://arxiv.org/pdf/2005.00625.pdf
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Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanismhttps://aaai.org/ojs/index.php/AAAI/article/view/3884
BibTexhttps://github.com/safe-graph/DGFraud/blob/master/reference/hacud.txt
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