Transferable Graph Neural

Transferable graph neural networks (GNNs) aim to leverage knowledge learned from one graph dataset to improve performance on different, related datasets, overcoming limitations of data scarcity and variability. Current research focuses on developing GNN architectures that effectively transfer knowledge across domains with differing structures and feature distributions, often employing techniques like hierarchical knowledge transfer, knowledge bridge learning, and prompt tuning. This research is significant because it enhances the generalizability and efficiency of GNNs across diverse applications, including drug discovery, supply chain risk assessment, and brain-computer interfaces, where data is often limited or heterogeneous.