56 citations · 104 across the 3 of their papers we have counts for
4 papers
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…
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…
BP-Transformer: Modelling Long-Range Context via Binary Partitioning
Zihao Ye, Qipeng Guo, Quan Gan +2
The Transformer model is widely successful on many natural language processing tasks. However, the quadratic complexity of self-attention limit its application on long text. In thi…
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang, Da Zheng, Zihao Ye +12
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and im…