  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.
