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cs.LG2025
Towards Pre-trained Graph Condensation via Optimal Transport
Yeyu Yan, Shuai Zheng, Wenjun Hui +5
Graph condensation (GC) aims to distill the original graph into a small-scale graph, mitigating redundancy and accelerating GNN training. However, conventional GC approaches heavil…
cs.LG2025
Dynamic Graph Condensation
Dong Chen, Shuai Zheng, Yeyu Yan +4
Recent research on deep graph learning has shifted from static to dynamic graphs, motivated by the evolving behaviors observed in complex real-world systems. However, the temporal…
cs.LG2024
OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
Xunkai Li, Yinlin Zhu, Boyang Pang +7
Federated graph learning (FGL) is a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing. This approach inher…