English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

To improve the robustness of graph neural networks (GNN), graph structurelearning (GSL) has attracted great interest due to the pervasiveness of noisein graph data. Many approaches have been proposed for GSL to jointly learn aclean graph structure and corresponding representations. To extend the previouswork, this paper proposes a novel regularized GSL approach, particularly withan alignment of feature information and graph information, which is motivatedmainly by our derived lower bound of node-level Rademacher complexity for GNNs.Additionally, our proposed approach incorporates sparse dimensional reductionto leverage low-dimensional node features that are relevant to the graphstructure. To evaluate the effectiveness of our approach, we conductexperiments on real-world graphs. The results demonstrate that our proposed GSLmethod outperforms several competitive baselines, especially in scenarios wherethe graph structures are heavily affected by noise. Overall, our researchhighlights the importance of integrating feature and graph informationalignment in GSL, as inspired by our derived theoretical result, and showcasesthe superiority of our approach in handling noisy graph structures throughcomprehensive experiments on real-world datasets.

Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix