Graph Condensation Method

Graph condensation aims to reduce the size of large graphs while preserving their essential properties for efficient graph neural network (GNN) training. Recent research focuses on improving the accuracy and speed of condensation, exploring methods that align gradients, feature distributions, and even the spectral properties of the original and condensed graphs, with some moving towards structure-free condensation. These advancements are significant because they enable the application of GNNs to massive datasets that were previously computationally intractable, impacting various fields requiring graph analysis.

Papers