# PyTorch Geometric — Graph Neural Network Library for PyTorch

> A library for deep learning on graphs and other irregular structures, featuring efficient mini-batch training and a broad collection of GNN operators.

## Install

Save as a script file and run:

# PyTorch Geometric — Graph Neural Network Library for PyTorch

## Quick Use
```bash
pip install torch-geometric
```
```python
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import GCNConv
dataset = Planetoid(root='/tmp/Cora', name='Cora')
```

## Introduction
PyTorch Geometric (PyG) is a library built on PyTorch for writing and training graph neural networks. It provides a unified API for working with graph-structured data, including point clouds, meshes, and molecules, making it straightforward to implement state-of-the-art GNN architectures.

## What PyTorch Geometric Does
- Implements 40+ GNN operators (GCN, GAT, GraphSAGE, GIN, and more)
- Provides efficient mini-batching for graphs of varying size
- Offers built-in benchmark datasets (Cora, PPI, QM9, OGB)
- Supports heterogeneous graphs with typed nodes and edges
- Includes utilities for graph sampling, clustering, and partitioning

## Architecture Overview
PyG extends PyTorch's tensor model with a Data object that stores node features, edge indices, and graph-level attributes in a sparse format. Message-passing layers inherit from MessagePassing, which abstracts neighbor aggregation into propagate, message, and update steps. A DataLoader collects variable-size graphs into diagonal block-sparse batches for GPU-parallel training.

## Self-Hosting & Configuration
- Requires PyTorch 1.12+ and a matching CUDA version for GPU support
- Install optional dependencies: torch-scatter, torch-sparse, torch-cluster
- Use pip or conda for installation; pre-built wheels available for major CUDA versions
- Configure num_workers in DataLoader for parallel data loading
- Supports distributed training via PyTorch DDP

## Key Features
- Composable message-passing framework for custom GNN layers
- Heterogeneous graph support with HeteroData and to_hetero transforms
- Scalable neighbor sampling for large-scale graphs (NeighborLoader)
- Integration with OGB (Open Graph Benchmark) leaderboards
- Explain module for GNN interpretability (GNNExplainer, Captum)

## Comparison with Similar Tools
- **DGL** — More backend-agnostic (supports TensorFlow, MXNet) but PyG has tighter PyTorch integration
- **Spektral** — Keras-based GNN library; smaller operator set
- **StellarGraph** — Focuses on enterprise use cases; less active development
- **GraphNets** — DeepMind's library built on TensorFlow/Sonnet; research-oriented

## FAQ
**Q: Does PyG support heterogeneous graphs?**
A: Yes. Use HeteroData and apply to_hetero() to convert homogeneous models to heterogeneous ones automatically.

**Q: Can PyG handle billion-edge graphs?**
A: Yes, via NeighborLoader and ClusterLoader which sample subgraphs for mini-batch training without loading the full graph.

**Q: Is GPU required?**
A: No. PyG runs on CPU, but GPU acceleration significantly speeds up training.

**Q: How does PyG differ from NetworkX?**
A: NetworkX is for general graph analysis. PyG is specifically for training neural networks on graph data with GPU support.

## Sources
- https://github.com/pyg-team/pytorch_geometric
- https://pytorch-geometric.readthedocs.io

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Source: https://tokrepo.com/en/workflows/44e567fd-42dc-11f1-9bc6-00163e2b0d79
Author: Script Depot