3 citations · 3 across the 4 of their papers we have counts for
4 papers
Demystifying Distributed Training of Graph Neural Networks for Link Prediction
Xin Huang, Chul-Ho Lee
Graph neural networks (GNNs) are powerful tools for solving graph-related problems. Distributed GNN frameworks and systems enhance the scalability of GNNs and accelerate model trai…
SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networks
Xin Huang, Weipeng Zhuo, Minh Phu Vuong +4
Recently, distributed GNN training frameworks, such as DistDGL and PyG, have been developed to enable training GNN models on large graphs by leveraging multiple GPUs in a distribut…
Controlling Epidemic Spread Under Immunization Delay Constraints
Shiju Li, Xin Huang, Chul-Ho Lee +1
In this paper, we study the problem of minimizing the spread of a viral epidemic when immunization takes a non-negligible amount of time to take into effect. Specifically, our prob…
Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass
Xin Huang, Jongryool Kim, Bradley Rees +1
Graph neural networks (GNNs) have received great attention due to their success in various graph-related learning tasks. Several GNN frameworks have then been developed for fast an…