2.0.3
Notes
Installation
Quick Start
Installation via Anaconda
Installation via Pip Wheels
Installation from Source
Frequently Asked Questions
Introduction by Example
Data Handling of Graphs
Common Benchmark Datasets
Mini-batches
Data Transforms
Learning Methods on Graphs
Creating Message Passing Networks
The “MessagePassing” Base Class
Implementing the GCN Layer
Implementing the Edge Convolution
Creating Your Own Datasets
Creating “In Memory Datasets”
Creating “Larger” Datasets
Frequently Asked Questions
Heterogeneous Graph Learning
Example Graph
Creating Heterogeneous Graphs
Heterogeneous Graph Transformations
Creating Heterogeneous GNNs
Heterogeneous Graph Samplers
Loading Graphs from CSV
Managing Experiments with GraphGym
Highlights
Why GraphGym?
Basic Usage
In-Depth Usage
Customizing GraphGym
Advanced Mini-Batching
Pairs of Graphs
Bipartite Graphs
Batching Along New Dimensions
Memory-Efficient Aggregations
TorchScript Support
Converting GNN Models
Creating Jittable GNN Operators
GNN Cheatsheet
Graph Neural Network Operators
Heterogeneous Graph Neural Network Operators
Hypergraph Neural Network Operators
Point Cloud Neural Network Operators
Colab Notebooks and Video Tutorials
External Resources
Package Reference
torch_geometric
torch_geometric.nn
Convolutional Layers
Dense Convolutional Layers
Normalization Layers
Global Pooling Layers
Pooling Layers
Dense Pooling Layers
Unpooling Layers
Models
Functional
Model Transformations
DataParallel Layers
torch_geometric.data
torch_geometric.loader
torch_geometric.datasets
torch_geometric.transforms
torch_geometric.utils
torch_geometric.graphgym
Workflow and Register Modules
Model Modules
Utility Modules
torch_geometric.profile
pytorch_geometric
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v: 2.0.3
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