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May 12, 2024 · This paper proposes a novel contrastive learning architecture for enhancing the performance of graph fraud detection.
Detecting fraudulent nodes from topological graphs is important in many real applications, such as financial fraud detection. This task is challenging due ...
May 14, 2024 · Variational Graph Autoencoders (VGAEs) are powerful models for unsupervised learning of node representations from graph data. In this work, we ...
MICA: Multi-channel Representation Refinement Contrastive Learning for Graph Fraud Detection. https://doi.org/10.1007/978-981-97-2421-5_3 ·.
MICA: Multi-channel Representation Refinement Contrastive Learning for Graph Fraud Detection. G. Wang, D. Tang, A. Shatsila, and X. Zhang.
Article "MICA: Multi-channel Representation Refinement Contrastive Learning for Graph Fraud Detection" Detailed information of the J-GLOBAL is an ...
MICA: Multi-channel Representation Refinement Contrastive Learning for Graph Fraud Detection. Guifeng Wang · Disheng Tang · Anatoli Shatsila · Xuecang Zhang.
MICA: multi-channel representation refinement contrastive learning for graph fraud detection. G Wang, D Tang, A Shatsila, X Zhang. Asia-Pacific Web (APWeb) ...
MICA: Multi-channel Representation Refinement Contrastive Learning for Graph Fraud Detection · Guifeng Wang, Disheng Tang, Anatoli Shatsila, Xuecang Zhang.
MICA: Multi-channel Representation Refinement Contrastive Learning for Graph Fraud Detection. G. Wang, D. Tang, A. Shatsila, und X. Zhang.