Optimal Propagation for Graph Neural Networks
This addresses the issue of suboptimal graph structures for GNNs in semi-supervised node classification, but it is incremental as it builds on existing propagation methods with optimization and approximation techniques.
The paper tackles the problem that fixed input graphs may not be optimal for downstream tasks in Graph Neural Networks (GNNs) by proposing a bi-level optimization approach to learn an optimal graph structure and propagation matrix, achieving superior efficacy and robustness over baseline methods.
Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input. However, the initial input graph might not be optimal in terms of specific downstream tasks, because of information scarcity, noise, adversarial attacks, or discrepancies between the distribution in graph topology, features, and groundtruth labels. In this paper, we propose a bi-level optimization approach for learning the optimal graph structure via directly learning the Personalized PageRank propagation matrix as well as the downstream semi-supervised node classification simultaneously. We also explore a low-rank approximation model for further reducing the time complexity. Empirical evaluations show the superior efficacy and robustness of the proposed model over all baseline methods.