torch-geometric

name	torch-geometric
description	Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.

PyTorch Geometric (PyG)

Routing Boundary

Use this skill only for PyTorch Geometric, torch_geometric, PyG, graph neural networks, GCN/GAT, graph classification, node classification, link prediction, and heterogeneous graph learning. Do not use it for generic neural networks, CNN/image classification, graph visualization, or molecule-only tasks unless PyG or graph neural network modeling is explicit.

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.

Naming Compatibility

Use torch-geometric as the canonical skill ID. Treat torch_geometric, PyG, and pytorch geometric as API or keyword spellings that route to this same skill, not as separate expert roles.

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

# Create a simple graph with 3 nodes
edge_index = torch.tensor([[0, 1, 1, 2],  # source nodes
                           [1, 0, 2, 1]], dtype=torch.long)  # target nodes
x = torch.tensor([[-1], [0], [1]], dtype=torch.float)  # node features

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

# Load Cora citation network
dataset = Planetoid(root='/tmp/Cora', name='Cora')
data = dataset[0]  # Get the first (and only) graph

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 graphs
• data.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 list: (0→1), (1→0), (1→2), (2→1)
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}")
    # batch.batch maps nodes to graphs

Building Graph Neural Networks

Message Passing Paradigm

GNNs in PyG follow a neighborhood aggregation scheme:

1. Transform node features
2. Propagate messages along edges
3. Aggregate messages from neighbors
4. 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')  # "add", "mean", or "max"
        self.lin = torch.nn.Linear(in_channels, out_channels)

    def forward(self, x, edge_index):
        # Add self-loops to adjacency matrix
        edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))

        # Transform node features
        x = self.lin(x)

        # Compute normalization
        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]

        # Propagate messages
        return self.propagate(edge_index, x=x, norm=norm)

    def message(self, x_j, norm):
        # x_j: features of source nodes
        return norm.view(-1, 1) * x_j

Key methods:

• forward(): Main entry point
• message(): Constructs messages from source to target nodes
• aggregate(): 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:

# Citation networks (node classification)
from torch_geometric.datasets import Planetoid
dataset = Planetoid(root='/tmp/Cora', name='Cora')  # or 'CiteSeer', 'PubMed'

# Graph classification
from torch_geometric.datasets import TUDataset
dataset = TUDataset(root='/tmp/ENZYMES', name='ENZYMES')

# Molecular datasets
from torch_geometric.datasets import QM9
dataset = QM9(root='/tmp/QM9')

# Large-scale datasets
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']  # Files needed in raw_dir

    @property
    def processed_file_names(self):
        return ['data.pt']  # Files in processed_dir

    def download(self):
        # Download raw data to self.raw_dir
        pass

    def process(self):
        # Read data, create Data objects
        data_list = []

        # Example: Create a simple graph
        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

# Load nodes
nodes_df = pd.read_csv('nodes.csv')
x = torch.tensor(nodes_df[['feat1', 'feat2']].values, dtype=torch.float)

# Load edges
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

# Load dataset
dataset = Planetoid(root='/tmp/Cora', name='Cora')
data = dataset[0]

# Create model
model = GCN(dataset.num_features, dataset.num_classes)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)

# Training
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}')

# Evaluation
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)

        # Global pooling (aggregate node features to graph-level)
        x = global_mean_pool(x, batch)

        x = self.lin(x)
        return F.log_softmax(x, dim=1)

# Load dataset
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)

# Training
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

# Create a neighbor sampler
train_loader = NeighborLoader(
    data,
    num_neighbors=[25, 10],  # Sample 25 neighbors for 1st hop, 10 for 2nd hop
    batch_size=128,
    input_nodes=data.train_mask,
)

# Training
model.train()
for batch in train_loader:
    optimizer.zero_grad()
    out = model(batch)
    # Only compute loss on seed nodes (first batch_size nodes)
    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()

# Add node features for different types
data['paper'].x = torch.randn(100, 128)  # 100 papers with 128 features
data['author'].x = torch.randn(200, 64)  # 200 authors with 64 features

# Add edges for different types (source_type, edge_type, target_type)
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

# Define homogeneous model
model = GNN(...)

# Convert to heterogeneous
model = to_hetero(model, data.metadata(), aggr='sum')

# Use as normal
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

# Single transform
transform = NormalizeFeatures()
dataset = Planetoid(root='/tmp/Cora', name='Cora', transform=transform)

# Compose multiple transforms
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

# Create explainer
explainer = Explainer(
    model=model,
    algorithm=GNNExplainer(epochs=200),
    explanation_type='model',  # or 'phenomenon'
    node_mask_type='attributes',
    edge_mask_type='object',
    model_config=dict(
        mode='multiclass_classification',
        task_level='node',
        return_type='log_probs',
    ),
)

# Generate explanation for a specific node
node_idx = 10
explanation = explainer(data.x, data.edge_index, index=node_idx)

# Visualize
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

# Undirected check
from torch_geometric.utils import is_undirected
print(f"Is undirected: {is_undirected(data.edge_index)}")

# Connected components
from torch_geometric.utils import connected_components
print(f"Connected components: {connected_components(data.edge_index)}")

# Contains self-loops
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 DataLoader
for batch in loader:
    batch = batch.to(device)
    # Train...

Save and Load Models

# Save
torch.save(model.state_dict(), 'model.pth')

# Load
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 capabilities
• references/datasets_reference.md: Comprehensive dataset catalog organized by category
• references/transforms_reference.md: All available transforms and their use cases
• references/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 matplotlib
• scripts/create_gnn_template.py: Generate boilerplate code for common GNN architectures
• scripts/benchmark_model.py: Benchmark model performance on standard datasets

Execute scripts directly or read them for implementation patterns.

Official Resources

• Documentation: https://pytorch-geometric.readthedocs.io/
• GitHub: https://github.com/pyg-team/pytorch_geometric
• Tutorials: https://pytorch-geometric.readthedocs.io/en/latest/get_started/introduction.html
• Examples: https://github.com/pyg-team/pytorch_geometric/tree/master/examples