12 citations · 15 across the 4 of their papers we have counts for
4 papers
GraphStorm: all-in-one graph machine learning framework for industry applications
Da Zheng, Xiang Song, Qi Zhu +13
Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challe…
Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures
Kun Wu, Mert Hidayetoğlu, Xiang Song +4
Relational graph neural networks (RGNNs) are graph neural networks with dedicated structures for modeling the different types of nodes and edges in heterogeneous graphs. While RGNN…
Nimble GNN Embedding with Tensor-Train Decomposition
Chunxing Yin, Da Zheng, Israt Nisa +3
This paper describes a new method for representing embedding tables of graph neural networks (GNNs) more compactly via tensor-train (TT) decomposition. We consider the scenario whe…
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs
Hongkuan Zhou, Da Zheng, Israt Nisa +3
Many real world graphs contain time domain information. Temporal Graph Neural Networks capture temporal information as well as structural and contextual information in the generate…