A heterogeneous-graph masked autoencoder (HGMAE) pre-training method yields a 0.831 Micro-F1 for bond default risk propagation prediction, 0.006 higher than GraphMAE, on a self-built 20M-node enterprise graph.
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Heterogeneous Graph Pre-training Based Model for Secure and Efficient Prediction of Default Risk Propagation among Bond Issuers
A heterogeneous-graph masked autoencoder (HGMAE) pre-training method yields a 0.831 Micro-F1 for bond default risk propagation prediction, 0.006 higher than GraphMAE, on a self-built 20M-node enterprise graph.