Graph Neural Networks (GNNs) have become a powerful tool for processing graph-structured data. However, real-world data often faces the problem of label noise and sparsity, which will greatly affect ...

Most existing graph neural networks (GNNs) are proposed without considering the selection bias in data, i.e., the inconsistent distribution between the training set with the test set. In reality, the ...