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

uv pip install torch_geometric

pyg-lib

torch-scatter

torch-sparse

torch-cluster

Core Concepts

Graph Data:

Data

and

HeteroData

A graph lives in a

Data

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

[2, num_edges]

edge_index[0]

edge_index[1]

.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]

[1,0]

For heterogeneous graphs, use

HeteroData

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)
• Graph classification: TUDataset (MUTAG, ENZYMES, PROTEINS, IMDB-BINARY), OGB (ogbg-molhiv)
• 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:

GCNConv

GATConv

GATv2Conv

SAGEConv

GINConv

TransformerConv

EdgeConv

RGCNConv

HGTConv

All conv layers accept

(x, edge_index)

edge_attr

Lazy Initialization

Use

-1

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

1. propagate()

   orchestrates the message passing

2. message()

   defines what info flows along each edge (the phi function)

3. aggregate()

   combines messages at each node (sum/mean/max)

4. 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

propagate()

_i

_j

message()

x=...

x_i

x_j

Read

references/message_passing.md

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
model.eval()
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

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

batch

global_mean_pool

global_max_pool

global_add_pool

Link Prediction

Split edges into train/val/test, use negative sampling:

from torch_geometric.transforms import RandomLinkSplit

transform = RandomLinkSplit(
    num_val=0.1,
    num_test=0.1,
    is_undirected=True,
    add_negative_train_samples=False,
)
train_data, val_data, test_data = transform(data)

# Encode nodes, then score edges
z = model.encode(train_data.x, train_data.edge_index)
# Positive edges
pos_score = (z[train_data.edge_label_index[0]] * z[train_data.edge_label_index[1]]).sum(dim=1)

Read

references/link_prediction.md

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

GraphSAINTSampler

ShaDowKHopSampler

torch.compile

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()

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)

2.

HeteroConv

from torch_geometric.nn import HeteroConv, GCNConv, SAGEConv, GATConv

conv = HeteroConv({
    ('paper', 'cites', 'paper'): GCNConv(-1, 64),
    ('author', 'writes', 'paper'): SAGEConv((-1, -1), 64),
    ('paper', 'rev_writes', 'author'): GATConv((-1, -1), 64, add_self_loops=False),
}, aggr='sum')

3. Native heterogeneous operators like

HGTConv

from torch_geometric.nn import HGTConv
conv = HGTConv(hidden_channels, hidden_channels, data.metadata(), num_heads=4)

Important for heterogeneous graphs:

• Use

  T.ToUndirected()

  to add reverse edge types for bidirectional message flow
• Disable

  add_self_loops

  in bipartite conv layers (different source/dest types) — use skip connections instead:

  conv(x, edge_index) + lin(x)

• For NeighborLoader on HeteroData, specify

  input_nodes

  as

  ('node_type', mask)

  tuple

• num_neighbors

  can be a dict keyed by edge type for fine-grained control

Read

references/heterogeneous.md

Custom Datasets

For loading your own data into PyG:

• Quick (no class needed): Create

  Data

  objects directly and pass a list to

  DataLoader

• Reusable (fits in RAM): Subclass

  InMemoryDataset

  — override

  raw_file_names

  ,

  processed_file_names

  ,

  download()

  ,

  process()

• Large (disk-backed): Subclass

  Dataset

  — also override

  len()

  and

  get()

• From CSV: Load node/edge tables with pandas, build mappings to consecutive indices, assemble into

  Data

  or

  HeteroData

• From NetworkX:

  from_networkx(G)

  converts a NetworkX graph directly
• From scipy sparse:

  from_scipy_sparse_matrix(adj)

  extracts edge_index

Read

references/custom_datasets.md

Explainability

PyG provides

torch_geometric.explain

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',
    ),
)

explanation = explainer(data.x, data.edge_index, index=10)
explanation.visualize_graph()           # Important subgraph
explanation.visualize_feature_importance(top_k=10)  # Feature importance

Available algorithms:

GNNExplainer

PGExplainer

CaptumExplainer

AttentionExplainer

Read

references/explainability.md

Common Pitfalls

1. edge_index shape: Must be

   [2, num_edges]

   , not

   [num_edges, 2]

   . Transpose if needed.
2. Forgetting activations: Conv layers don't include ReLU/etc — add them manually.
3. Self-loops in hetero bipartite: Don't use

   add_self_loops=True

   when source and dest node types differ. Use skip connections instead.
4. NeighborLoader slicing: Only the first

   batch.batch_size

   nodes are your seed nodes. Slice predictions and labels accordingly.
5. Undirected graphs: If your graph is undirected, include edges in both directions in

   edge_index

   , or use

   T.ToUndirected()

   .
6. Lazy init: Models with

   -1

   input channels need one forward pass with

   torch.no_grad()

   before training to initialize parameters.
7. Global pooling for graph tasks: Use

   global_mean_pool(x, batch)

   (not manual reshape) to aggregate node features to graph-level.
8. 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.