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.

翻译：为提升图神经网络（GNN）的鲁棒性，图结构学习（GSL）因图数据中噪声的普遍存在而备受关注。现有多种GSL方法被提出，旨在联合学习干净的图结构及其对应表示。为拓展先前工作，本文提出一种新型正则化GSL方法，该方法主要通过我们推导的GNN节点级Rademacher复杂度下界，实现特征信息与图信息对齐。此外，所提方法引入稀疏降维技术以利用与图结构相关的低维节点特征。为评估方法有效性，我们在真实世界图上进行实验。结果表明，尤其在图结构受噪声严重干扰的场景下，我们提出的GSL方法优于多个竞争基线。总体而言，本文基于理论推导结果，强调了在GSL中整合特征与图信息对齐的重要性，并通过真实数据集上的综合实验展示了方法处理噪声图结构的优越性。