Guide for building Graph Neural Networks with PyTorch Geometric (PyG). Use this skill whenever the user asks about graph neural networks, GNNs, node classification, link prediction, graph classification, message passing networks, heterogeneous graphs, neighbor sampling, or any task involving torch_geometric / PyG. Also trigger when you see imports from torch_geometric, or the user mentions graph convolutions (GCN, GAT, GraphSAGE, GIN), graph data structures, or working with relational/network data. Even if the user just says 'graph learning' or 'geometric deep learning', use this skill.
# PyTorch Geometric (PyG)
PyG is the standard library for Graph Neural Networks built on PyTorch. It provides data structures for graphs, 60+ GNN layer implementations, scalable mini-batch training, and support for heterogeneous graphs.
Install: `uv add torch_geometric` (or `uv pip install torch_geometric`; requires PyTorch). Optional: `pyg-lib`, `torch-scatter`, `torch-sparse`, `torch-cluster` for accelerated ops.
## Core Concepts
### Graph Data: `Data` and `HeteroData`
A graph lives in a `Data` object. The key attributes:
```python
from torch_geometric.data import Data
data = Data(
x=node_features, # [num_nodes, num_node_features]
edge_index=edge_index, # [2, num_edges] — COO format, dtype=torch.long
edge_attr=edge_features, # [num_edges, num_edge_features]
y=labels, # node-level [num_nodes, *] or graph-level [1, *]
pos=positions, # [num_nodes, num_dimensions] (for point clouds/spatial)
)
```
**`edge_index` format is critical**: it's a `[2, num_edges]` tensor where `edge_index[0]` = source nodes, `edge_index[1]` = target nodes. It is NOT a list of tuples. If you have edge pairs as rows, transpose and call `.contiguous()`:Sign in to view the full prompt.
Sign In