GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights

Understand

Graph condensation (GC) is an emerging technique designed to learn a significantly smaller graph that retains the essential information of the original graph.

• This condensed graph has shown promise in accelerating graph neural networks while preserving performance comparable to those achieved with the original, larger graphs.
• Additionally, this technique facilitates downstream applications like neural architecture search and deepens our understanding of redundancies in large graphs.
• Despite the rapid development of GC methods, particularly for node classification, a unified evaluation framework is still lacking to systematically compare different GC methods or clarify key design choices for improving their effectiveness.