Graph Condensation via Homophily Node Refining and Fine-Grained Distribution Matching

The remarkable success of GNNs has provoked the challenge of high computational and memory overhead when training with large-scale graphs. As a promising solution, graph condensation is committed to constructing synthetic graphs with significantly smaller size, which are expected to preserve the essential characteristics of the original ones. During this process, a core problem is how to accurately portray and align the data distribution structures between the original graph space and the synthe