718 citations · 854 across the 22 of their papers we have counts for
5 papers · 1 filter
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
Da Zheng, Chao Ma, Minjie Wang +6
Graph neural networks (GNN) have shown great success in learning from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection,…
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
Yuwei Hu, Zihao Ye, Minjie Wang +6
Graph neural networks (GNNs) are gaining increasing popularity as a promising approach to machine learning on graphs. Unlike traditional graph workloads where each vertex/edge is a…
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing
Vassilis N. Ioannidis, Da Zheng, George Karypis
Predicting interactions among heterogenous graph structured data has numerous applications such as knowledge graph completion, recommendation systems and drug discovery. Often time…
PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs
Vassilis N. Ioannidis, Da Zheng, George Karypis
Learning unsupervised node embeddings facilitates several downstream tasks such as node classification and link prediction. A node embedding is universal if it is designed to be us…
DGL-KE: Training Knowledge Graph Embeddings at Scale
Da Zheng, Xiang Song, Chao Ma +6
Knowledge graphs have emerged as a key abstraction for organizing information in diverse domains and their embeddings are increasingly used to harness their information in various…