UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks

Understand

In the graph domain, deep graph networks based on Message Passing Neural Networks (MPNNs) or Graph Transformers often cause over-smoothing of node features, limiting their expressive capacity.

• Many upsampling techniques involving node and edge manipulation have been proposed to mitigate this issue.
• However, these methods are often heuristic, resulting in extensive manual labor and suboptimal performance and lacking a universal integration strategy.
• In this study, we introduce UniGAP, a universal and adaptive graph upsampling framework to mitigate over-smoothing in node classification tasks.