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F2gnn: An adaptive filter with feature segmentation for graph-based fraud detection
Graph Neural Networks (GNNs) have received remarkable success in identifying fraudulent
activities on graphs. Most approaches leverage the full user feature together and aggregate …
activities on graphs. Most approaches leverage the full user feature together and aggregate …
Self explainable graph convolutional recurrent network for spatio-temporal forecasting
J García-Sigüenza, M Curado, F Llorens-Largo… - Machine Learning, 2025 - Springer
Artificial intelligence (AI) is transforming industries and decision-making processes, but
concerns about transparency and fairness have increased. Explainable artificial intelligence …
concerns about transparency and fairness have increased. Explainable artificial intelligence …
[PDF][PDF] Non-negative Tucker decomposition with double constraints for multiway dimensionality reduction
X Gao, L Lu, Q Liu - AIMS Mathematics, 2024 - aimspress.com
Nonnegative Tucker decomposition (NTD) is one of the renowned techniques in feature
extraction and representation for nonnegative high-dimensional tensor data. The main focus …
extraction and representation for nonnegative high-dimensional tensor data. The main focus …
[PDF][PDF] Graph-based Semi-Supervised Learning for Fraud Detection in Finance
NK Alapati - 2024 - researchgate.net
The financial field is an area that does not suffer from vulnerability to various types of
financial fraud, with severe losses associated with individuals and organizations. It needs to …
financial fraud, with severe losses associated with individuals and organizations. It needs to …