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Introduction by Example
Colab Notebooks and Video Tutorials
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Design of Graph Neural Networks
Working with Graph Datasets
Use-Cases & Applications
Scaling GNNs via Neighbor Sampling
Point Cloud Processing
Explaining Graph Neural Networks
Shallow Node Embeddings
Distributed Training
Advanced Concepts
Advanced Mini-Batching
Memory-Efficient Aggregations
Hierarchical Neighborhood Sampling
Compiled Graph Neural Networks
TorchScript Support
Scaling Up GNNs via Remote Backends
Managing Experiments with GraphGym
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.transforms
torch_geometric.utils
torch_geometric.explain
torch_geometric.metrics
torch_geometric.distributed
torch_geometric.contrib
torch_geometric.graphgym
torch_geometric.profile
Cheatsheets
GNN Cheatsheet
Dataset Cheatsheet
External Resources
External Resources
pytorch_geometric
Use-Cases & Applications
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Use-Cases & Applications
Scaling GNNs via Neighbor Sampling
Point Cloud Processing
Explaining Graph Neural Networks
Shallow Node Embeddings
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