6 citations · 6 across the 2 of their papers we have counts for
2 papers
cs.DC2024
HopGNN: Boosting Distributed GNN Training Efficiency via Feature-Centric Model Migration
Weijian Chen, Shuibing He, Haoyang Qu +1
Distributed training of graph neural networks (GNNs) has become a crucial technique for processing large graphs. Prevalent GNN frameworks are model-centric, necessitating the trans…
cs.LG2024★ 6 cited
A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges
ZhengZhao Feng, Rui Wang, TianXing Wang +3
Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to capture structural, temporal, and contextual relationships in dynamic graphs simultaneously, leading…