3 papers
cs.LG2026
Heterogeneous Graph Condensation via Role-Aware Clustering
Fuyan Ou, Yulin Hu, Ye Yuan
Heterogeneous Graph Neural Networks (HGNNs) have exhibited remarkable efficacy in modeling complex systems with multiple types of nodes and relations, yet their training on large-s…
cs.LG2026
An Efficient and Scalable Graph Condensation with Structure-Preserving
Yulin Hu, Fuyan Ou, Ye Yuan
Graph condensation (GC) is pivotal for enabling Graph Neural Networks (GNNs) deployment in resource-constrained scenarios by compressing large-scale graphs into compact synthetic c…
cs.LG2025
HGC-Herd: Efficient Heterogeneous Graph Condensation via Representative Node Herding
Fuyan Ou, Siqi Ai, Yulin Hu
Heterogeneous graph neural networks (HGNNs) have demonstrated strong capability in modeling complex semantics across multi-type nodes and relations. However, their scalability to l…