GNNs are ideally suited for the Graphcore IPU architecture. Get started today with a wide range of GNN applications for your industry or your scientific research with GNNs .Try GNNs today
Graph neural networks (GNNs) are AI models designed to derive insights from unstructured data described by graphs. GNNs are an ideal fit for the Graphcore IPU, designed from the ground up for AI expressed as graphs. Unlike conventional CNNs, GNNs address the challenge of working with data in irregular domains. There are many applications for GNNs including molecular analysis, drug discovery, fraud detection, stock market prediction, traffic forecasting and much more.
Graph intelligence is a rapid growth segment within AI forecast to make up a sizeable percentage of the overall AI market in the coming years. Many businesses and organizations are discovering the potential of GNNs, and are applying them in a number of areas to drive innovation within their industry.
Take data intelligence to the next level with graph neural networks for applications across drug discovery, molecular modeling, chemistry, life sciences and biotechnology.
Whether you’re working on protein sequencing or predicting patient outcomes, IPUs are designed to help you get deeper insights faster from graph neural networks.
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.
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