  Teaching Graph Neural Networks (GNNs) to accurately classify nodes under
severely noisy labels is an important problem in real-world graph learning
applications, but is currently underexplored. Although pairwise training
methods have demonstrated promise in supervised metric learning and
unsupervised contrastive learning, they remain less studied on noisy graphs,
where the structural pairwise interactions (PI) between nodes are abundant and
thus might benefit label noise learning rather than the pointwise methods. This
paper bridges the gap by proposing a pairwise framework for noisy node
classification on graphs, which relies on the PI as a primary learning proxy in
addition to the pointwise learning from the noisy node class labels. Our
proposed framework PI-GNN contributes two novel components: (1) a
confidence-aware PI estimation model that adaptively estimates the PI labels,
which are defined as whether the two nodes share the same node labels, and (2)
a decoupled training approach that leverages the estimated PI labels to
regularize a node classification model for robust node classification.
Extensive experiments on different datasets and GNN architectures demonstrate
the effectiveness of PI-GNN, yielding a promising improvement over the
state-of-the-art methods. Code is publicly available at
https://github.com/TianBian95/pi-gnn.
