RobGC: Towards Robust Graph Condensation

Understand

Graph neural networks (GNNs) have attracted widespread attention for their impressive capability of graph representation learning.

• However, the increasing prevalence of large-scale graphs presents a significant challenge for GNN training due to their computational demands, limiting the applicability of GNNs in various scenarios.
• In response to this challenge, graph condensation (GC) is proposed as a promising acceleration solution, focusing on generating an informative compact graph that enables efficient training of GNNs while retaining performance.
• Despite the potential to accelerate GNN training, existing GC methods overlook the quality of large training graphs during both the training and inference stages.