EXGC: Bridging Efficiency and Explainability in Graph Condensation

Understand

Graph representation learning on vast datasets, like web data, has made significant strides.

• However, the associated computational and storage overheads raise concerns.
• In sight of this, Graph condensation (GCond) has been introduced to distill these large real datasets into a more concise yet information-rich synthetic graph.
• Despite acceleration efforts, existing GCond methods mainly grapple with efficiency, especially on expansive web data graphs.