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Found 3 papers, 1 papers with code
Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures
Relational graph neural networks (RGNNs) are graph neural networks with dedicated structures for modeling the different types of nodes and edges in heterogeneous graphs.
Nimble GNN Embedding with Tensor-Train Decomposition
This paper describes a new method for representing embedding tables of graph neural networks (GNNs) more compactly via tensor-train (TT) decomposition.
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs
Our temporal parallel sampler achieves an average of 173x speedup on a multi-core CPU compared with the baselines.
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