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Found 5 papers, 0 papers with code
Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs
Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily.
GRANDE: a neural model over directed multigraphs with application to anti-money laundering
The application of graph representation learning techniques to the area of financial risk management (FRM) has attracted significant attention recently.
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention
With the frequent happening of privacy leakage and the enactment of privacy laws across different countries, data owners are reluctant to directly share their raw data and labels with any other party.
Learning Feature Disentanglement and Dynamic Fusion for Recaptured Image Forensic
Image recapture seriously breaks the fairness of artificial intelligent (AI) systems, which deceives the system by recapturing others' images.
RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring
Accordingly, we for the first time propose to model the biased credit scoring data with Multi-Task Learning (MTL).
