Beyond Homophily and Homogeneity Assumption: Relation-Based Frequency Adaptive Graph Neural Networks
Abstract: Graph neural networks (GNNs) have been playing important roles in various graph-related tasks. However, most existing GNNs are based on the assumption of homophily, so they cannot be ...
Abstract: This study aims to address the challenges of financial price prediction in high-frequency trading (HFT) by introducing a novel continual learning framework based on factor predictors via ...
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