AI RESEARCH
An Efficient and Scalable Graph Condensation with Structure-Preserving
arXiv CS.LG
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ArXi:2605.31016v1 Announce Type: new Graph condensation (GC) is pivotal for enabling Graph Neural Networks (GNNs) deployment in resource-constrained scenarios by compressing large-scale graphs into compact synthetic counterparts. Existing GC methods commonly suffer from computational inefficiency due to coupled optimization as well as encountering poor generalization across GNN architectures.