PyTorch Geometric (PyG)
Overview
PyTorch Geometric is a library built on PyTorch for developing and training Graph Neural Networks (GNNs). Apply this skill for deep learning on graphs and irregular structures, including mini-batch processing, multi-GPU training, and geometric deep learning applications.
When to Use This Skill
This skill should be used when working with:
- Graph-based machine learning: Node classification, graph classification, link prediction
- Molecular property prediction: Drug discovery, chemical property prediction
- Social network analysis: Community detection, influence prediction
- Citation networks: Paper classification, recommendation systems
- 3D geometric data: Point clouds, meshes, molecular structures
- Heterogeneous graphs: Multi-type nodes and edges (e.g., knowledge graphs)
- Large-scale graph learning: Neighbor sampling, distributed training
Quick Start
Installation
uv pip install torch_geometric
For additional dependencies (sparse operations, clustering):
uv pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html
Basic Graph Creation
import torch
from torch_geometric.data import Data
edge_index = torch.tensor([[0, 1, 1, 2],
[1, 0, 2, 1]], dtype=torch.long)
x = torch.tensor([[-1], [0], [1]], dtype=torch.float)
data = Data(x=x, edge_index=edge_index)
print(f"Nodes: {data.num_nodes}, Edges: {data.num_edges}")
Loading a Benchmark Dataset
from torch_geometric.datasets import Planetoid
dataset = Planetoid(root='/tmp/Cora', name='Cora')
data = dataset[0]
print(f"Dataset: {dataset}")
print(f"Nodes: {data.num_nodes}, Edges: {data.num_edges}")
print(f"Features: {data.num_node_features}, Classes: {dataset.num_classes}")
Core Concepts
Data Structure
PyG represents graphs using the torch_geometric.data.Data class with these key attributes:
data.x: Node feature matrix [num_nodes, num_node_features]data.edge_index: Graph connectivity in COO format [2, num_edges]data.edge_attr: Edge feature matrix [num_edges, num_edge_features] (optional)data.y: Target labels for nodes or graphsdata.pos: Node spatial positions [num_nodes, num_dimensions] (optional)- Custom attributes: Can add any attribute (e.g.,
data.train_mask, data.batch)
Important: These attributes are not mandatory—extend Data objects with custom attributes as needed.
Edge Index Format
Edges are stored in COO (coordinate) format as a [2, num_edges] tensor:
- First row: source node indices
- Second row: target node indices
edge_index = torch.tensor([[0, 1, 1, 2],
[1, 0, 2, 1]], dtype=torch.long)
Mini-Batch Processing
PyG handles batching by creating block-diagonal adjacency matrices, concatenating multiple graphs into one large disconnected graph:
- Adjacency matrices are stacked diagonally
- Node features are concatenated along the node dimension
- A
batch vector maps each node to its source graph
- No padding needed—computationally efficient
from torch_geometric.loader import DataLoader
loader = DataLoader(dataset, batch_size=32, shuffle=True)
for batch in loader:
print(f"Batch size: {batch.num_graphs}")
print(f"Total nodes: {batch.num_nodes}")
Building Graph Neural Networks
Message Passing Paradigm
GNNs in PyG follow a neighborhood aggregation scheme:
- Transform node features
- Propagate messages along edges
- Aggregate messages from neighbors
- Update node representations
Using Pre-Built Layers
PyG provides 40+ convolutional layers. Common ones include:
GCNConv (Graph Convolutional Network):
from torch_geometric.nn import GCNConv
import torch.nn.functional as F
class GCN(torch.nn.Module):
def __init__(self, num_features, num_classes):
super().__init__()
self.conv1 = GCNConv(num_features, 16)
self.conv2 = GCNConv(16, num_classes)
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = self.conv1(x, edge_index)
x = F.relu(x)
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
GATConv (Graph Attention Network):
from torch_geometric.nn import GATConv
class GAT(torch.nn.Module):
def __init__(self, num_features, num_classes):
super().__init__()
self.conv1 = GATConv(num_features, 8, heads=8, dropout=0.6)
self.conv2 = GATConv(8 * 8, num_classes, heads=1, concat=False, dropout=0.6)
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = F.dropout(x, p=0.6, training=self.training)
x = F.elu(self.conv1(x, edge_index))
x = F.dropout(x, p=0.6, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
GraphSAGE:
from torch_geometric.nn import SAGEConv
class GraphSAGE(torch.nn.Module):
def __init__(self, num_features, num_classes):
super().__init__()
self.conv1 = SAGEConv(num_features, 64)
self.conv2 = SAGEConv(64, num_classes)
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = self.conv1(x, edge_index)
x = F.relu(x)
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
Custom Message Passing Layers
For custom layers, inherit from MessagePassing:
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree
class CustomConv(MessagePassing):
def __init__(self, in_channels, out_channels):
super().__init__(aggr='add')
self.lin = torch.nn.Linear(in_channels, out_channels)
def forward(self, x, edge_index):
edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))
x = self.lin(x)
row, col = edge_index
deg = degree(col, x.size(0), dtype=x.dtype)
deg_inv_sqrt = deg.pow(-0.5)
norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]
return self.propagate(edge_index, x=x, norm=norm)
def message(self, x_j, norm):
return norm.view(-1, 1) * x_j
Key methods:
forward(): Main entry pointmessage(): Constructs messages from source to target nodesaggregate(): Aggregates messages (usually don't override—set aggr parameter)update(): Updates node embeddings after aggregation
Variable naming convention: Appending _i or _j to tensor names automatically maps them to target or source nodes.
Working with Datasets
Loading Built-in Datasets
PyG provides extensive benchmark datasets:
from torch_geometric.datasets import Planetoid
dataset = Planetoid(root='/tmp/Cora', name='Cora')
from torch_geometric.datasets import TUDataset
dataset = TUDataset(root='/tmp/ENZYMES', name='ENZYMES')
from torch_geometric.datasets import QM9
dataset = QM9(root='/tmp/QM9')
from torch_geometric.datasets import Reddit
dataset = Reddit(root='/tmp/Reddit')
Check references/datasets_reference.md for a comprehensive list.
Creating Custom Datasets
For datasets that fit in memory, inherit from InMemoryDataset:
from torch_geometric.data import InMemoryDataset, Data
import torch
class MyOwnDataset(InMemoryDataset):
def __init__(self, root, transform=None, pre_transform=None):
super().__init__(root, transform, pre_transform)
self.load(self.processed_paths[0])
@property
def raw_file_names(self):
return ['my_data.csv']
@property
def processed_file_names(self):
return ['data.pt']
def download(self):
pass
def process(self):
data_list = []
edge_index = torch.tensor([[0, 1], [1, 0]], dtype=torch.long)
x = torch.randn(2, 16)
y = torch.tensor([0], dtype=torch.long)
data = Data(x=x, edge_index=edge_index, y=y)
data_list.append(data)
.pre_filter :
data_list = [d d data_list .pre_filter(d)]
.pre_transform :
data_list = [.pre_transform(d) d data_list]
.save(data_list, .processed_paths[])
For large datasets that don't fit in memory, inherit from Dataset and implement len() and get(idx).
Loading Graphs from CSV
import pandas as pd
import torch
from torch_geometric.data import HeteroData
nodes_df = pd.read_csv('nodes.csv')
x = torch.tensor(nodes_df[['feat1', 'feat2']].values, dtype=torch.float)
edges_df = pd.read_csv('edges.csv')
edge_index = torch.tensor([edges_df['source'].values,
edges_df['target'].values], dtype=torch.long)
data = Data(x=x, edge_index=edge_index)
Training Workflows
Node Classification (Single Graph)
import torch
import torch.nn.functional as F
from torch_geometric.datasets import Planetoid
dataset = Planetoid(root='/tmp/Cora', name='Cora')
data = dataset[0]
model = GCN(dataset.num_features, dataset.num_classes)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
model.train()
for epoch in range(200):
optimizer.zero_grad()
out = model(data)
loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
if epoch % 10 == 0:
print(f'Epoch {epoch}, Loss: {loss.item():.4f}')
model.eval()
pred = model(data).argmax(dim=1)
correct = (pred[data.test_mask] == data.y[data.test_mask]).sum()
acc = int(correct) / int(data.test_mask.sum())
print(f'Test Accuracy: {acc:.4f}')
Graph Classification (Multiple Graphs)
from torch_geometric.datasets import TUDataset
from torch_geometric.loader import DataLoader
from torch_geometric.nn import global_mean_pool
class GraphClassifier(torch.nn.Module):
def __init__(self, num_features, num_classes):
super().__init__()
self.conv1 = GCNConv(num_features, 64)
self.conv2 = GCNConv(64, 64)
self.lin = torch.nn.Linear(64, num_classes)
def forward(self, data):
x, edge_index, batch = data.x, data.edge_index, data.batch
x = self.conv1(x, edge_index)
x = F.relu(x)
x = self.conv2(x, edge_index)
x = F.relu(x)
x = global_mean_pool(x, batch)
x = self.lin(x)
return F.log_softmax(x, dim=1)
dataset = TUDataset(root='/tmp/ENZYMES', name='ENZYMES')
loader = DataLoader(dataset, batch_size=32, shuffle=True)
model = GraphClassifier(dataset.num_features, dataset.num_classes)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
model.train()
for epoch in range(100):
total_loss = 0
for batch loader:
optimizer.zero_grad()
out = model(batch)
loss = F.nll_loss(out, batch.y)
loss.backward()
optimizer.step()
total_loss += loss.item()
epoch % == :
()
Large-Scale Graphs with Neighbor Sampling
For large graphs, use NeighborLoader to sample subgraphs:
from torch_geometric.loader import NeighborLoader
train_loader = NeighborLoader(
data,
num_neighbors=[25, 10],
batch_size=128,
input_nodes=data.train_mask,
)
model.train()
for batch in train_loader:
optimizer.zero_grad()
out = model(batch)
loss = F.nll_loss(out[:batch.batch_size], batch.y[:batch.batch_size])
loss.backward()
optimizer.step()
Important:
- Output subgraphs are directed
- Node indices are relabeled (0 to batch.num_nodes - 1)
- Only use seed node predictions for loss computation
- Sampling beyond 2-3 hops is generally not feasible
Advanced Features
Heterogeneous Graphs
For graphs with multiple node and edge types, use HeteroData:
from torch_geometric.data import HeteroData
data = HeteroData()
data['paper'].x = torch.randn(100, 128)
data['author'].x = torch.randn(200, 64)
data['author', 'writes', 'paper'].edge_index = torch.randint(0, 200, (2, 500))
data['paper', 'cites', 'paper'].edge_index = torch.randint(0, 100, (2, 300))
print(data)
Convert homogeneous models to heterogeneous:
from torch_geometric.nn import to_hetero
model = GNN(...)
model = to_hetero(model, data.metadata(), aggr='sum')
out = model(data.x_dict, data.edge_index_dict)
Or use HeteroConv for custom edge-type-specific operations:
from torch_geometric.nn import HeteroConv, GCNConv, SAGEConv
class HeteroGNN(torch.nn.Module):
def __init__(self, metadata):
super().__init__()
self.conv1 = HeteroConv({
('paper', 'cites', 'paper'): GCNConv(-1, 64),
('author', 'writes', 'paper'): SAGEConv((-1, -1), 64),
}, aggr='sum')
self.conv2 = HeteroConv({
('paper', 'cites', 'paper'): GCNConv(64, 32),
('author', 'writes', 'paper'): SAGEConv((64, 64), 32),
}, aggr='sum')
def forward(self, x_dict, edge_index_dict):
x_dict = self.conv1(x_dict, edge_index_dict)
x_dict = {key: F.relu(x) for key, x in x_dict.items()}
x_dict = self.conv2(x_dict, edge_index_dict)
return x_dict
Transforms
Apply transforms to modify graph structure or features:
from torch_geometric.transforms import NormalizeFeatures, AddSelfLoops, Compose
transform = NormalizeFeatures()
dataset = Planetoid(root='/tmp/Cora', name='Cora', transform=transform)
transform = Compose([
AddSelfLoops(),
NormalizeFeatures(),
])
dataset = Planetoid(root='/tmp/Cora', name='Cora', transform=transform)
Common transforms:
- Structure:
ToUndirected, AddSelfLoops, RemoveSelfLoops, KNNGraph, RadiusGraph
- Features:
NormalizeFeatures, NormalizeScale, Center
- Sampling:
RandomNodeSplit, RandomLinkSplit
- Positional Encoding:
AddLaplacianEigenvectorPE, AddRandomWalkPE
See references/transforms_reference.md for the full list.
Model Explainability
PyG provides explainability tools to understand model predictions:
from torch_geometric.explain import Explainer, GNNExplainer
explainer = Explainer(
model=model,
algorithm=GNNExplainer(epochs=200),
explanation_type='model',
node_mask_type='attributes',
edge_mask_type='object',
model_config=dict(
mode='multiclass_classification',
task_level='node',
return_type='log_probs',
),
)
node_idx = 10
explanation = explainer(data.x, data.edge_index, index=node_idx)
print(f'Node {node_idx} explanation:')
print(f'Important edges: {explanation.edge_mask.topk(5).indices}')
print(f'Important features: {explanation.node_mask[node_idx].topk(5).indices}')
Pooling Operations
For hierarchical graph representations:
from torch_geometric.nn import TopKPooling, global_mean_pool
class HierarchicalGNN(torch.nn.Module):
def __init__(self, num_features, num_classes):
super().__init__()
self.conv1 = GCNConv(num_features, 64)
self.pool1 = TopKPooling(64, ratio=0.8)
self.conv2 = GCNConv(64, 64)
self.pool2 = TopKPooling(64, ratio=0.8)
self.lin = torch.nn.Linear(64, num_classes)
def forward(self, data):
x, edge_index, batch = data.x, data.edge_index, data.batch
x = F.relu(self.conv1(x, edge_index))
x, edge_index, _, batch, _, _ = self.pool1(x, edge_index, None, batch)
x = F.relu(self.conv2(x, edge_index))
x, edge_index, _, batch, _, _ = self.pool2(x, edge_index, None, batch)
x = global_mean_pool(x, batch)
x = self.lin(x)
return F.log_softmax(x, dim=1)
Common Patterns and Best Practices
Check Graph Properties
from torch_geometric.utils import is_undirected
print(f"Is undirected: {is_undirected(data.edge_index)}")
from torch_geometric.utils import connected_components
print(f"Connected components: {connected_components(data.edge_index)}")
from torch_geometric.utils import contains_self_loops
print(f"Has self-loops: {contains_self_loops(data.edge_index)}")
GPU Training
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
data = data.to(device)
for batch in loader:
batch = batch.to(device)
Save and Load Models
torch.save(model.state_dict(), 'model.pth')
model = GCN(num_features, num_classes)
model.load_state_dict(torch.load('model.pth'))
model.eval()
Layer Capabilities
When choosing layers, consider these capabilities:
- SparseTensor: Supports efficient sparse matrix operations
- edge_weight: Handles one-dimensional edge weights
- edge_attr: Processes multi-dimensional edge features
- Bipartite: Works with bipartite graphs (different source/target dimensions)
- Lazy: Enables initialization without specifying input dimensions
See the GNN cheatsheet at references/layer_capabilities.md.
Resources
Bundled References
This skill includes detailed reference documentation:
references/layers_reference.md: Complete listing of all 40+ GNN layers with descriptions and capabilitiesreferences/datasets_reference.md: Comprehensive dataset catalog organized by categoryreferences/transforms_reference.md: All available transforms and their use casesreferences/api_patterns.md: Common API patterns and coding examples
Scripts
Utility scripts are provided in scripts/:
scripts/visualize_graph.py: Visualize graph structure using networkx and matplotlibscripts/create_gnn_template.py: Generate boilerplate code for common GNN architecturesscripts/benchmark_model.py: Benchmark model performance on standard datasets
Execute scripts directly or read them for implementation patterns.
Official Resources
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