Node Classification
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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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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The RAHG experimental data includes four public datasets and one self-built dataset. The experimental process of RAHG on these five datasets is also recorded.
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