Graph Neural Networks with IPU

TGN: Temporal Graph Networks is a dynamic GNN model for training on the IPU using PyG (PyTorch Geometric)

Training a GNN to do Fraud Detection using Relational Graph Convolution Network (RGCN) on IPUs with PyG (PyTorch Geometric)

GNN-based model in PyG (PyTorch Geometric) developed for modelling quantum interactions between atoms in a molecule

An efficient algorithm for training deep and large Graph Convolutional Networks using PyG (PyTorch Geometric)

Graph Isomorphism Network (GIN) is used to perform graph classification for molecular property prediction using PyG (PyTorch Geometric)

Bellman-Ford networks (NBFnet) is a GNN model used for link prediction in homogeneous and heterogeneous graphs implemented in PyG (PyTorch Geometric)

A hybrid GNN/Transformer for training Molecular Property Prediction using IPUs on the PCQM4Mv2 dataset. Winner of the Open Graph Benchmark Large-Scale Challenge.

A hybrid GNN/Transformer for Molecular Property Prediction inference using IPUs trained on the PCQM4Mv2 dataset. Winner of the Open Graph Benchmark Large-Scale Challenge.

Knowledge graph embedding (KGE) for link-prediction training on IPUs using Poplar with the WikiKG90Mv2 dataset. Winner of the Open Graph Benchmark Large-Scale Challenge.

Knowledge graph embedding (KGE) for link-prediction inference on IPUs using Poplar with the WikiKG90Mv2 dataset. Winner of the Open Graph Benchmark Large-Scale Challenge.