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PyG Documentation — pytorch_geometric documentation PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data
Introduction by Example — pytorch_geometric documentation After learning about data handling, datasets, loader and transforms in PyG, it’s time to implement our first graph neural network! We will use a simple GCN layer and replicate the experiments on the Cora citation dataset
Installation — pytorch_geometric documentation For earlier PyTorch versions (torch<=2 5 0), you can install PyG via Anaconda for all major OS, and CUDA combinations If you have not yet installed PyTorch, install it via conda install as described in its official documentation
torch_geometric. nn — pytorch_geometric documentation To facilitate further experimentation and unify the concepts of aggregation within GNNs across both MessagePassing and global readouts, we have made the concept of Aggregation a first-class principle in PyG
Explaining Graph Neural Networks — pytorch_geometric documentation PyG (2 3 and beyond) provides the torch_geometric explain package for first-class GNN explainability support that currently includes a flexible interface to generate a variety of explanations via the Explainer class, several underlying explanation algorithms including, e g , GNNExplainer, PGExplainer and CaptumExplainer,
Colab Notebooks and Video Tutorials — pytorch_geometric documentation The Stanford CS224W course has collected a set of graph machine learning tutorial blog posts, fully realized with PyG Students worked on projects spanning all kinds of tasks, model architectures and applications
Design of Graph Neural Networks — pytorch_geometric documentation CPU Affinity for PyG Workloads Package Reference torch_geometric torch_geometric nn torch_geometric data torch_geometric loader torch_geometric sampler torch_geometric datasets torch_geometric llm torch_geometric transforms torch_geometric utils torch_geometric explain torch_geometric metrics torch_geometric distributed torch_geometric contrib
Graph Transformer — pytorch_geometric documentation Graph Transformer Transformer is an effictive architecture in natural language processing and computer vision Recently, there have been some applications (Grover, GraphGPS, etc) that combine transformers on graphs In this tutorial, we will present how to build a graph transformer model via PyG See our webinar for in-depth learning on this topic
torch_geometric. nn. conv. GCNConv — pytorch_geometric documentation conv GCNConv class GCNConv (in_channels: int, out_channels: int, improved: bool = False, cached: bool = False, add_self_loops: Optional[bool] = None, normalize: bool = True, bias: bool = True, **kwargs) [source] Bases: MessagePassing The graph convolutional operator from the “Semi-supervised Classification with Graph Convolutional Networks” paper