Label-informed Graph Structure Learning for Node Classification
This addresses the problem of improving graph structure learning for node classification tasks, but it is incremental as it builds on existing methods by adding label information.
The paper tackles the problem of GNNs being sensitive to graph structure quality by proposing a label-informed graph structure learning framework that incorporates label information through a class transition matrix, and it shows results that outperform or match state-of-the-art baselines on seven node classification benchmark datasets.
Graph Neural Networks (GNNs) have achieved great success among various domains. Nevertheless, most GNN methods are sensitive to the quality of graph structures. To tackle this problem, some studies exploit different graph structure learning strategies to refine the original graph structure. However, these methods only consider feature information while ignoring available label information. In this paper, we propose a novel label-informed graph structure learning framework which incorporates label information explicitly through a class transition matrix. We conduct extensive experiments on seven node classification benchmark datasets and the results show that our method outperforms or matches the state-of-the-art baselines.