This source did not publish a separate summary. Review SKILL.md before using the skill.
SKILL.md
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.
Installation
Tested against torch-geometric 2.7.x (Oct 2025). Requires Python 3.10+ and PyTorch 2.6+.
# 1. Install PyTorch first (match your CUDA/CPU setup — see https://pytorch.org/get-started/locally/)
uv pip install torch
# 2. Core PyG (no extension wheels required for basic usage)
uv pip install torch_geometric
Optional accelerated ops (pyg-lib, torch-scatter, torch-sparse, torch-cluster) are not required for basic PyG usage (since PyG 2.3). Install version-matched wheels from the PyG wheel index after checking your PyTorch and CUDA versions:
python -c "import torch; print(torch.__version__, torch.version.cuda)"
# Then install wheels for your torch+CUDA combo, e.g.:
uv pip install pyg-lib torch-scatter torch-sparse torch-cluster \
-f https://data.pyg.org/whl/torch-2.8.0+cu128.html
Conda: the pyg conda channel is no longer maintained for PyTorch >2.5 — use uv pip install and the wheel index above instead.
PyG 2.7 notes
PyG 2.7 dropped Python 3.9 and PyTorch ≤2.5. See the 2.7.0 release notes for PyTorch 2.6–2.8 compatibility tables. torch_geometric.distributed is deprecated — use standard torch.distributed DDP (see references/scaling.md).
Core Concepts
Graph Data: and
Data
HeteroData
A graph lives in a Data object. The key attributes:
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():
# If edges are [[src1, dst1], [src2, dst2], ...] — transpose first:
edge_index = edge_pairs.t().contiguous()
For undirected graphs, include both directions: edge (0,1) needs both [0,1] and [1,0] in edge_index.
For heterogeneous graphs, use HeteroData — see the Heterogeneous Graphs section below.
Datasets
PyG bundles many standard datasets that auto-download and preprocess:
from torch_geometric.datasets import Planetoid, TUDataset
# Single-graph node classification (Cora, Citeseer, Pubmed)
dataset = Planetoid(root='./data', name='Cora')
data = dataset[0] # single graph with train/val/test masks
# Multi-graph classification (ENZYMES, MUTAG, IMDB-BINARY, etc.)
dataset = TUDataset(root='./data', name='ENZYMES')
# dataset[0], dataset[1], ... are individual graphs
Common datasets by task:
Node classification: Planetoid (Cora/Citeseer/Pubmed), OGB (ogbn-arxiv, ogbn-products, ogbn-mag)
Link prediction: OGB (ogbl-collab, ogbl-citation2)
Molecular: QM7, QM9, MoleculeNet
Point cloud/mesh: ShapeNet, ModelNet10/40, FAUST
Transforms
Transforms preprocess or augment graph data, analogous to torchvision transforms:
import torch_geometric.transforms as T
# Common transforms
T.NormalizeFeatures() # Row-normalize node features to sum to 1
T.ToUndirected() # Add reverse edges to make graph undirected
T.AddSelfLoops() # Add self-loop edges
T.KNNGraph(k=6) # Build k-NN graph from point cloud positions
T.RandomJitter(0.01) # Random noise augmentation on positions
T.Compose([...]) # Chain multiple transforms
# Apply as pre_transform (once, saved to disk) or transform (every access)
dataset = ShapeNet(root='./data', pre_transform=T.KNNGraph(k=6),
transform=T.RandomJitter(0.01))
Building GNN Models
Quick Start: Using Built-in Layers
The fastest way to build a GNN — stack conv layers from torch_geometric.nn:
import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
class GCN(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels):
super().__init__()
self.conv1 = GCNConv(in_channels, hidden_channels)
self.conv2 = GCNConv(hidden_channels, out_channels)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index).relu()
x = F.dropout(x, p=0.5, training=self.training)
x = self.conv2(x, edge_index)
return x
Important: PyG conv layers do NOT include activation functions — apply them yourself after each layer. This is by design for flexibility.
Choosing a Conv Layer
Pick based on your task and graph structure:
Layer
Best for
Key idea
GCNConv
Homogeneous, semi-supervised node classification
Spectral-inspired, degree-normalized aggregation
GATConv / GATv2Conv
When neighbor importance varies
Attention-weighted messages
SAGEConv
Large graphs, inductive settings
Sampling-friendly, learnable aggregation
GINConv
Graph classification, maximizing expressiveness
As powerful as WL test
TransformerConv
Rich edge features, complex interactions
Multi-head attention with edge features
EdgeConv
Point clouds, dynamic graphs
MLP on edge features (x_i, x_j - x_i)
RGCNConv
Heterogeneous with many relation types
Relation-specific weight matrices
HGTConv
Heterogeneous graphs
Type-specific attention
All conv layers accept (x, edge_index) at minimum. Many also accept edge_attr for edge features.
Lazy Initialization
Use -1 for input channels to let PyG infer dimensions automatically — especially useful for heterogeneous models:
conv = SAGEConv((-1, -1), 64) # Input dims inferred on first forward pass
# Initialize lazy modules:
with torch.no_grad():
out = model(data.x, data.edge_index)
High-Level Model APIs
For common architectures, PyG provides ready-made model classes:
from torch_geometric.nn import GraphSAGE, GCN, GAT, GIN
model = GraphSAGE(
in_channels=dataset.num_features,
hidden_channels=64,
out_channels=dataset.num_classes,
num_layers=2,
)
Custom Layers via MessagePassing
To implement a novel GNN layer, subclass MessagePassing. The framework is:
propagate() orchestrates the message passing
message() defines what info flows along each edge (the phi function)
aggregate() combines messages at each node (sum/mean/max)
update() transforms the aggregated result (the gamma function)
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree
class MyConv(MessagePassing):
def __init__(self, in_channels, out_channels):
super().__init__(aggr='add') # "add", "mean", or "max"
self.lin = torch.nn.Linear(in_channels, out_channels)
def forward(self, x, edge_index):
# Pre-processing before message passing
x = self.lin(x)
# Start message passing
return self.propagate(edge_index, x=x)
def message(self, x_j):
# x_j: features of source nodes for each edge [num_edges, features]
# The _j suffix auto-indexes source nodes, _i indexes target nodes
return x_j
The _i / _j convention: any tensor passed to propagate() can be auto-indexed by appending _i (target/central node) or _j (source/neighbor node) in the message() signature. So if you pass x=... to propagate, you can access x_i and x_j in message().
Read references/message_passing.md for the full GCN and EdgeConv implementation examples.
Task-Specific Patterns
Node Classification
# Full-batch training on a single graph (e.g., Cora)
model.train()
for epoch in range(200):
optimizer.zero_grad()
out = model(data.x, data.edge_index)
loss = F.cross_entropy(out[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
# Evaluation — train(False) puts the model in inference mode (disables dropout/BN)
model.train(False)
pred = model(data.x, data.edge_index).argmax(dim=1)
acc = (pred[data.test_mask] == data.y[data.test_mask]).float().mean()
Graph Classification
Multiple graphs — use DataLoader for mini-batching and global pooling to get graph-level representations:
from torch_geometric.loader import DataLoader
from torch_geometric.nn import GCNConv, global_mean_pool
loader = DataLoader(dataset, batch_size=32, shuffle=True)
class GraphClassifier(torch.nn.Module):
def __init__(self, in_ch, hidden_ch, out_ch):
super().__init__()
self.conv1 = GCNConv(in_ch, hidden_ch)
self.conv2 = GCNConv(hidden_ch, hidden_ch)
self.lin = torch.nn.Linear(hidden_ch, out_ch)
def forward(self, x, edge_index, batch):
x = self.conv1(x, edge_index).relu()
x = self.conv2(x, edge_index).relu()
x = global_mean_pool(x, batch) # [num_graphs_in_batch, hidden_ch]
return self.lin(x)
# Training loop
for data in loader:
out = model(data.x, data.edge_index, data.batch)
loss = F.cross_entropy(out, data.y)
PyG's DataLoader batches multiple graphs by creating block-diagonal adjacency matrices. The batch tensor maps each node to its graph index. Pooling ops (global_mean_pool, global_max_pool, global_add_pool) use this to aggregate per-graph.
Link Prediction
Split edges into train/val/test, use negative sampling:
Read references/link_prediction.md for the complete link prediction guide: GAE/VGAE autoencoders, full training loops, LinkNeighborLoader for large graphs, heterogeneous link prediction, and evaluation metrics.
Scaling to Large Graphs
For graphs that don't fit in GPU memory, use neighbor sampling via NeighborLoader:
from torch_geometric.loader import NeighborLoader
train_loader = NeighborLoader(
data,
num_neighbors=[15, 10], # Sample 15 neighbors in hop 1, 10 in hop 2
batch_size=128, # Number of seed nodes per batch
input_nodes=data.train_mask, # Which nodes to sample from
shuffle=True,
)
for batch in train_loader:
batch = batch.to(device)
out = model(batch.x, batch.edge_index)
# Only use first batch_size nodes for loss (these are the seed nodes)
loss = F.cross_entropy(out[:batch.batch_size], batch.y[:batch.batch_size])
Key points about NeighborLoader:
num_neighbors list length should match GNN depth (number of message passing layers)
Seed nodes are always the first batch.batch_size nodes in the output
batch.n_id maps relabeled indices back to original node IDs
Works for both Data and HeteroData
For link prediction, use LinkNeighborLoader instead
Sampling more than 2-3 hops is generally infeasible (exponential blowup)
Other scalability options: ClusterLoader (ClusterGCN), GraphSAINTSampler, ShaDowKHopSampler. For multi-GPU training, DDP, PyTorch Lightning integration, and torch.compile support, read references/scaling.md.
Heterogeneous Graphs
For graphs with multiple node and edge types (social networks, knowledge graphs, recommendation):
from torch_geometric.data import HeteroData
data = HeteroData()
# Node features — indexed by node type string
data['user'].x = torch.randn(1000, 64)
data['movie'].x = torch.randn(500, 128)
# Edge indices — indexed by (src_type, edge_type, dst_type) triplet
data['user', 'rates', 'movie'].edge_index = torch.randint(0, 500, (2, 3000))
data['user', 'follows', 'user'].edge_index = torch.randint(0, 1000, (2, 5000))
# Access convenience dicts
data.x_dict # {'user': tensor, 'movie': tensor}
data.edge_index_dict # {('user','rates','movie'): tensor, ...}
data.metadata() # ([node_types], [edge_types])
Three ways to build heterogeneous GNNs
1. Auto-convert with to_hetero() — write a homogeneous model, convert automatically:
from torch_geometric.nn import SAGEConv, to_hetero
class GNN(torch.nn.Module):
def __init__(self, hidden_channels, out_channels):
super().__init__()
self.conv1 = SAGEConv((-1, -1), hidden_channels)
self.conv2 = SAGEConv((-1, -1), out_channels)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index).relu()
x = self.conv2(x, edge_index)
return x
model = GNN(64, dataset.num_classes)
model = to_hetero(model, data.metadata(), aggr='sum')
# Now accepts dicts:
out = model(data.x_dict, data.edge_index_dict)
Use (-1, -1) for bipartite input channels (source, target may differ). Lazy init handles the rest.
2. HeteroConv wrapper — different conv per edge type:
Available algorithms: GNNExplainer (optimization-based), PGExplainer (parametric, trained), CaptumExplainer (gradient-based via Captum), AttentionExplainer (attention weights). Works for both homogeneous and heterogeneous graphs.
Read references/explainability.md for all algorithms, heterogeneous explanations, evaluation metrics, and PGExplainer training.
Common Pitfalls
edge_index shape: Must be [2, num_edges], not [num_edges, 2]. Transpose if needed.
Forgetting activations: Conv layers don't include ReLU/etc — add them manually.
Self-loops in hetero bipartite: Don't use add_self_loops=True when source and dest node types differ. Use skip connections instead.
NeighborLoader slicing: Only the first batch.batch_size nodes are your seed nodes. Slice predictions and labels accordingly.
Undirected graphs: If your graph is undirected, include edges in both directions in edge_index, or use T.ToUndirected().
Lazy init: Models with -1 input channels need one forward pass with torch.no_grad() before training to initialize parameters.
Global pooling for graph tasks: Use global_mean_pool(x, batch) (not manual reshape) to aggregate node features to graph-level.
num_neighbors alignment: Keep len(num_neighbors) equal to the number of GNN layers. More hops than layers wastes compute; fewer means wasted model capacity.
