SCN_GNN: A GNN-based fraud detection algorithm combining strong node and graph topology information

[1]  Mingyang Zhou,et al.  Temporal burstiness and collaborative camouflage aware fraud detection , 2023, Inf. Process. Manag..

[2]  Jianliang Gao,et al.  Dual-Augment Graph Neural Network for Fraud Detection , 2022, CIKM.

[3]  Jochen De Weerdt,et al.  CATCHM: A novel network-based credit card fraud detection method using node representation learning , 2022, Decis. Support Syst..

[4]  Honglong Chen,et al.  MAFI: GNN-Based Multiple Aggregators and Feature Interactions Network for Fraud Detection Over Heterogeneous Graph , 2022, IEEE Transactions on Big Data.

[5]  Enas F. Rawashdeh,et al.  Class balancing framework for credit card fraud detection based on clustering and similarity-based selection (SBS) , 2022, International Journal of Information Technology.

[6]  Qing He,et al.  AUC-oriented Graph Neural Network for Fraud Detection , 2022, WWW.

[7]  Chuan Zhou,et al.  H2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic Connections , 2022, WWW.

[8]  Xiangnan He,et al.  Rumor detection with self-supervised learning on texts and social graph , 2022, Frontiers Comput. Sci..

[9]  Zhengya Sun,et al.  Improving Fraud Detection via Hierarchical Attention-based Graph Neural Network , 2022, J. Inf. Secur. Appl..

[10]  Hongzhi Yin,et al.  Decoupling Representation Learning and Classification for GNN-based Anomaly Detection , 2021, SIGIR.

[11]  Yufan Zeng,et al.  RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection , 2021, Applied Sciences.

[12]  Xiang Ao,et al.  Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection , 2021, WWW.

[13]  Philip S. Yu,et al.  Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks , 2021, ACM Trans. Inf. Syst..

[14]  Philip S. Yu,et al.  Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters , 2020, CIKM.

[15]  Kok-Leong Ong,et al.  Fraud detection: A systematic literature review of graph-based anomaly detection approaches , 2020, Decis. Support Syst..

[16]  Philip S. Yu,et al.  Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection , 2020, SIGIR.

[17]  Chunming Wu,et al.  ASA: Adversary Situation Awareness via Heterogeneous Graph Convolutional Networks , 2020, WWW.

[18]  Wenbing Huang,et al.  Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks , 2020, AAAI.

[19]  Dong Li,et al.  Spam Review Detection with Graph Convolutional Networks , 2019, CIKM.

[20]  Yu Huang,et al.  FdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review System , 2019, WWW.

[21]  Jie Zhang,et al.  Collusive Opinion Fraud Detection in Online Reviews , 2017, ACM Trans. Web.

[22]  Bogdan Carbunar,et al.  Search Rank Fraud and Malware Detection in Google Play , 2017, IEEE Transactions on Knowledge and Data Engineering.

[23]  Leman Akoglu,et al.  Collective Opinion Spam Detection: Bridging Review Networks and Metadata , 2015, KDD.

[24]  Jure Leskovec,et al.  From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews , 2013, WWW.

[25]  Ling Liu,et al.  Fraud Detection in Online Consumer Reviews , 2008, Decis. Support Syst..