  Graph Neural Networks (GNNs) have emerged as a powerful tool for
representation learning on graphs, but they often suffer from overfitting and
label noise issues, especially when the data is scarce or imbalanced. Different
from the paradigm of previous methods that rely on single-node confidence, in
this paper, we introduce a novel Class-wise Selection for Graph Neural
Networks, dubbed CSGNN, which employs a neighbor-aggregated latent space to
adaptively select reliable nodes across different classes. Specifically, 1) to
tackle the class imbalance issue, we introduce a dynamic class-wise selection
mechanism, leveraging the clustering technique to identify clean nodes based on
the neighbor-aggregated confidences. In this way, our approach can avoid the
pitfalls of biased sampling which is common with global threshold techniques.
2) To alleviate the problem of noisy labels, built on the concept of the
memorization effect, CSGNN prioritizes learning from clean nodes before noisy
ones, thereby iteratively enhancing model performance while mitigating label
noise. Through extensive experiments, we demonstrate that CSGNN outperforms
state-of-the-art methods in terms of both effectiveness and robustness.
