Graph Neural Networks (GNN)

GNN Logo Graph Neural Networks (GNNs) are a class of deep learning models designed to process data represented as graphs. They excel at learning from structured data with complex relationships, making them powerful tools for social network analysis, molecular structure prediction, and recommendation systems. GNNs can capture both local and global graph structure through message passing between nodes.

Core Concepts

GNNs are built on several key concepts that enable them to effectively process graph data.

  • Network Architecture

    The structure of a GNN consists of:

    • Node feature transformation
    • Message passing layers
    • Graph-level pooling
    • Readout functions

  • Key Operations

    The main operations in GNNs include:

    • Node feature aggregation
    • Edge feature processing
    • Graph convolution
    • Attention mechanisms

Key Components

  • Message passing layers
  • Node feature encoders
  • Edge feature encoders
  • Graph pooling layers
  • Readout functions

  • Loss functions
  • Optimizers
  • Learning rate scheduling
  • Batch normalization
  • Dropout

  • Graph attention networks
  • Graph convolutional networks
  • Graph isomorphism networks
  • Dynamic GNNs
  • Heterogeneous GNNs

External Resources

PyTorch Geometric

Library for GNN implementation

 GNN Tutorial

Interactive introduction to GNNs

 GNN Course

Deep learning course on GNNs

Related Topics

Deep Neural Networks

Foundation of GNNs
Graph Theory

Mathematical foundation
Recommender Systems

Key application of GNNs

Implementation Examples

GNN with PyTorch Geometric

import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, global_mean_pool

class GNN(torch.nn.Module):
    def __init__(self, num_node_features, num_classes):
        super(GNN, self).__init__()
        self.conv1 = GCNConv(num_node_features, 64)
        self.conv2 = GCNConv(64, 64)
        self.conv3 = GCNConv(64, 64)
        self.classifier = torch.nn.Linear(64, num_classes)
        
    def forward(self, x, edge_index, batch):
        # Node feature transformation
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, p=0.2, training=self.training)
        
        x = self.conv2(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, p=0.2, training=self.training)
        
        x = self.conv3(x, edge_index)
        x = F.relu(x)
        
        # Graph-level pooling
        x = global_mean_pool(x, batch)
        
        # Classification
        x = self.classifier(x)
        return F.log_softmax(x, dim=1)

# Example usage
num_node_features = 16  # Number of features per node
num_classes = 2        # Binary classification

model = GNN(num_node_features, num_classes)
print(model)