Datasets & Competitions
The python code of Graph Neural Network (GRN). Recent studies have shown that the predictive performance of graph neural networks (GNNs) is inconsistent and varies across different experimental runs, even with identical parameters. The prediction variability limits GNNs' applicability, and the underlying reasons remain unclear. We have identified a key factor contributing to this issue: the oscillation of some nodes' predicted classes during GNN training.
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Graph neural networks (GNNs) are widely applied in graph data modeling. However, existing GNNs are often trained in a task-driven manner that fails to fully capture the intrinsic nature of the graph structure, resulting in sub-optimal node and graph representations. To address this limitation, we propose a novel \textbf{G}raph structure \textbf{P}rompt \textbf{L}earning method (GPL) to enhance the training of GNNs, which is inspired by prompt mechanisms in natural language processing.
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The RAHG experimental data includes six public datasets and one self-built dataset. The experimental process of RAHG on these seven datasets is also recorded in it
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