  To improve the robustness of graph neural networks (GNN), graph structure
learning (GSL) has attracted great interest due to the pervasiveness of noise
in graph data. Many approaches have been proposed for GSL to jointly learn a
clean graph structure and corresponding representations. To extend the previous
work, this paper proposes a novel regularized GSL approach, particularly with
an alignment of feature information and graph information, which is motivated
mainly by our derived lower bound of node-level Rademacher complexity for GNNs.
Additionally, our proposed approach incorporates sparse dimensional reduction
to leverage low-dimensional node features that are relevant to the graph
structure. To evaluate the effectiveness of our approach, we conduct
experiments on real-world graphs. The results demonstrate that our proposed GSL
method outperforms several competitive baselines, especially in scenarios where
the graph structures are heavily affected by noise. Overall, our research
highlights the importance of integrating feature and graph information
alignment in GSL, as inspired by our derived theoretical result, and showcases
the superiority of our approach in handling noisy graph structures through
comprehensive experiments on real-world datasets.
