36 citations · 83 across the 5 of their papers we have counts for
9 papers
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs
Jialin Dong, Da Zheng, Lin F. Yang +1
Graph neural networks (GNNs) are powerful tools for learning from graph data and are widely used in various applications such as social network recommendation, fraud detection, and…
Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs
Saurav Manchanda, Da Zheng, George Karypis
Graph convolutional network (GCN) based approaches have achieved significant progress for solving complex, graph-structured problems. GCNs incorporate the graph structure informati…
Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks
Balasubramaniam Srinivasan, Da Zheng, George Karypis
Graph representation learning has made major strides over the past decade. However, in many relational domains, the input data are not suited for simple graph representations as th…
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…