Graph Neural Networks

Graph neural networks are deep learning models built to work with graph-structured data. Instead of rows and columns, they focus on nodes and edges. This allows graph neural networks to capture relationships, dependencies, and structure that traditional neural networks often miss. 

They use a message-passing approach where each node learns by gathering information from its neighbors. This makes graph neural networks effective for tasks such as social network analysis, recommendation systems, and molecular research, where connections between entities matter as much as the entities themselves. 

In this blog, you will learn what graph neural networks are, its architecture, how they work, and where they are used. 

Enroll in upGrad’s Artificial Intelligence Courses and learn how to implement GNN models and apply them to real-world problems through hands-on projects. 

What Are Graph Neural Networks and Why They Matter 

Graph neural networks work on data where relationships are the core signal. Instead of treating data points independently, they model how entities connect and influence each other. 

This makes them suitable for problems where structure carries meaning. 

How Graph Neural Networks Represent Data 

They use graphs as input. 

• Nodes represent entities such as users, products, or molecules 
• Edges represent relationships such as interactions, similarity, or transactions 
• Node features store attributes linked to each entity 

Learning happens by updating node information based on connected nodes. 

Also Read: Capsule Neural Networks: What is, How it Works, Architecture & Components 

Why Traditional Models Fall Short 

Standard neural networks assume fixed-size inputs. They struggle with irregular structures and changing connections. 

Graph neural networks solve this by: 

• Handling variable graph sizes 
• Preserving relationship structure 
• Learning patterns across connections 

This allows better predictions when links matter more than isolated values. 

Why Graph Neural Networks Matter Today 

Modern data is highly connected. 

• Social platforms rely on user networks 
• Financial systems track linked transactions 
• Scientific data maps complex interactions 

Graph neural networks extract insights from these connections, making them critical for real-world, relationship-driven problems. 

Also Read: Discover How Neural Networks Work to Transform Modern AI!

How Graph Neural Networks Work Step by Step 

Graph neural networks follow a structured learning flow built around connections in data. Instead of treating each data point in isolation, they allow linked nodes to share information. 

Below is a clear, step-by-step breakdown of how this process works. 

Step 1: Graph Input 

The process begins with a graph representation of data. 

• Nodes represent entities such as users, products, or molecules 
• Edges define relationships between those entities 
• Node features store attributes linked to each node 

This graph becomes the direct input to the model. 

Also Read: Neural Network Architecture 

Step 2: Neighbor Message Passing 

Each node starts interacting with its neighbors. 

• Nodes send information to directly connected nodes 
• Shared data helps capture local structure 
• Relationships guide how information flows 

This step allows nodes to understand their immediate context. 

Step 3: Message Aggregation 

Nodes collect information from neighbors. 

• Incoming messages are combined 
• Useful patterns are retained 
• Unnecessary noise is reduced 

The result is a summarized view of surrounding nodes. 

Also Read: Recurrent Neural Networks: Introduction, Problems, LSTMs Explained 

Step 4: Node Update 

Each node updates its representation. 

• Original features mix with aggregated information 
• Updated values store richer context 
• Representations become more informative 

This step refines how each node understands its role in the graph. 

Step 5: Layer-by-Layer Learning 

The message passing and update process repeats. 

• Information travels beyond immediate neighbors 
• Nodes gain broader graph awareness 
• Structural patterns become clearer 

More layers allow deeper learning across connections. 

Step 6: Final Output Generation 

The model produces predictions based on learned representations. 

• Node-level outputs for classification tasks 
• Edge-level outputs for relationship prediction 
• Graph-level outputs for whole-graph analysis 

Each output reflects knowledge built through repeated information sharing and updates. 

Also Read: Convolutional Neural Networks: Ultimate Guide for Beginners 

Common Types of Graph Neural Networks 

Different graph neural network models are designed to handle different graph structures and problem sizes. Each type follows the same core idea of learning from connected nodes, but they differ in how information is aggregated and updated. 

Graph Convolutional Networks (GCNs) 

Graph Convolutional Networks are one of the earliest and most widely used models. 

• Neighbor node features are combined using a fixed aggregation rule 
• Information spreads evenly across connected nodes 
• Best suited for smaller or moderately sized graphs 

GCNs work well for tasks like node classification in citation networks and social graphs. 

Also Read: Explore 8 Must-Know Types of Neural Networks in AI Today! 

Graph Attention Networks (GATs) 

Graph Attention Networks improve flexibility by using attention. 

• Not all neighbors are treated equally 
• Important neighbors receive higher attention scores 
• Less relevant connections receive lower weight 

This makes GATs effective when graph connections are noisy or uneven in importance. 

GraphSAGE 

GraphSAGE is designed for scalability. 

• Samples a fixed number of neighbors 
• Learns how to aggregate information instead of using fixed rules 
• Handles large and dynamic graphs efficiently 

It is commonly used in recommendation systems and real-time applications. 

Also Read: Top 40 AI Projects to Build in 2026 for Career Growth 

Graph Isomorphism Networks (GINs) 

Graph Isomorphism Networks focus on expressive power. 

• Strong at distinguishing graph structures 
• Uses powerful aggregation functions 
• Performs well on graph classification tasks 

GINs are often used in chemical and biological graph analysis. 

Comparison Overview 

Model	Key Strength	Best Use Case
GCN	Simple and stable	Small to medium graphs
GAT	Adaptive neighbor weighting	Noisy or complex graphs
GraphSAGE	Scalable learning	Large graphs
GIN	High expressiveness	Graph-level tasks

Each type builds the same foundation but solves different challenges depending on graph size, complexity, and task requirements. 

Also Read: AI Course Fees and Career Opportunities in India for 2026 

Implementation of a Graph Neural Network (GNN) 

Below is a simple and beginner-friendly implementation of a Graph Neural Network using PyTorch Geometric. 

The example focuses on node classification, which is one of the most common GNN tasks. 

Step 1: Install Required Libraries 

pip install torch torch-geometric 

Step 2: Import Libraries 

import torch 
import torch.nn.functional as F 
from torch_geometric.nn import GCNConv 
from torch_geometric.datasets import Planetoid 

Step 3: Load Graph Dataset 

Here we use the Cora citation network. 

dataset = Planetoid(root='data', name='Cora') 
data = dataset[0] 

What this graph contains: 

• data.x → node features 
• data.edge_index → graph connections 
• data.y → node labels 

Also Read: AI Engineer Salary in India [For Beginners & Experienced] 

Step 4: Define the GNN Model 

class GNN(torch.nn.Module): 
    def __init__(self): 
        super(GNN, self).__init__() 
        self.conv1 = GCNConv(dataset.num_features, 16) 
        self.conv2 = GCNConv(16, dataset.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 = self.conv2(x, edge_index) 
 
        return x 

What happens here: 

• First layer gathers neighbor information 
• ReLU adds non-linearity 
• Second layer produces class scores 

Step 5: Train the Model 

model = GNN() 
optimizer = torch.optim.Adam(model.parameters(), lr=0.01) 
loss_fn = torch.nn.CrossEntropyLoss() 
 
for epoch in range(200): 
    model.train() 
    optimizer.zero_grad() 
 
    out = model(data) 
    loss = loss_fn(out[data.train_mask], data.y[data.train_mask]) 
    loss.backward() 
    optimizer.step() 
 
    if epoch % 20 == 0: 
        print(f"Epoch {epoch}, Loss: {loss.item():.4f}") 

Output: 

Epoch 0, Loss: 1.9471 

Epoch 20, Loss: 0.1207 

Epoch 40, Loss: 0.0100 

Epoch 60, Loss: 0.0038 

Epoch 80, Loss: 0.0026 

Epoch 100, Loss: 0.0020 

Epoch 120, Loss: 0.0016 

Epoch 140, Loss: 0.0013 

Epoch 160, Loss: 0.0011 

Epoch 180, Loss: 0.0010 

Training logic: 

• Forward pass on the graph 
• Compute loss on training nodes 
• Backpropagate and update weights 

Also Read: Best 30 Artificial Intelligence Projects 

Step 6: Evaluate the GNN 

model.eval() 
pred = model(data).argmax(dim=1) 
correct = (pred[data.test_mask] == data.y[data.test_mask]).sum() 
accuracy = int(correct) / int(data.test_mask.sum()) 
 
print(f"Test Accuracy: {accuracy:.4f}") 
 

Output: 

Test Accuracy: 0.7750 

Step 7: Visualize GNN Predictions 

Visualization helps you see how the GNN separates nodes after learning. 

Here, node colors represent predicted classes. 

import matplotlib.pyplot as plt 
from sklearn.manifold import TSNE 
 
model.eval() 
embeddings = model(data).detach() 
 
tsne = TSNE(n_components=2) 
node_embeddings_2d = tsne.fit_transform(embeddings) 
 
plt.figure(figsize=(8, 6)) 
plt.scatter( 
    node_embeddings_2d[:, 0], 
    node_embeddings_2d[:, 1], 
    c=pred, 
    cmap="tab10", 
    s=15 
) 
plt.title("GNN Node Embeddings Visualization") 
plt.show() 
 

Output: 

What This Visualization Shows 

• Each point represents a node 
• Color shows the predicted class 
• Nearby points share similar representations 

This makes it easier to understand how the GNN groups related nodes after learning from graph structure and node features. 

What This GNN Is Learning 

• Nodes learn from neighboring nodes 
• Information spreads through graph layers 
• Final embeddings capture structure and features 

This implementation shows how graph neural networks are built and trained in practice, using real graph data and a clean learning flow. 

Also Read: What Is Production System in AI? Key Features Explained 

Real-World Use Cases of Graph Neural Networks 

Graph neural networks are used when relationships between data points matter as much as the data itself. Below are practical, real-world use cases where graph-based learning delivers clear value. 

Social Network Analysis 

Social platforms rely heavily on connected data. 

• Users are nodes 
• Friendships, follows, and interactions are edges 
• Communities form through dense connections 

Graph neural networks help with friend suggestions, community detection, and influence analysis by learning from how users are connected and interacting over time. 

Also Read: Job Opportunities in AI: Salaries, Skills & Careers 

Recommendation Systems 

Modern recommendation engines use graphs to model behavior. 

• Users connect to products, movies, or content 
• Items connect to similar items 
• Interactions strengthen or weaken relationships 

This structure helps deliver more relevant recommendations by understanding both user behavior and item relationships. 

Fraud Detection 

Fraud often hides in complex connection patterns. 

• Accounts link to transactions 
• Devices connect multiple users 
• Suspicious loops or clusters emerge 

Graph neural networks detect abnormal relationship patterns that traditional models often miss. 

Search and Knowledge Graphs 

Search engines and assistants use knowledge graphs. 

• Entities act as nodes 
• Facts and relationships act as edges 
• Context flows across connections 

This improves entity understanding, ranking quality, and answer relevance. 

Also Read: The Future Scope of Artificial Intelligence in 2026 and Beyond 

Healthcare and Biology 

Biological data is naturally graph-based. 

• Molecules contain atoms as nodes 
• Proteins interact through complex networks 
• Biological pathways form large graphs 

Graph neural networks support drug discovery, protein interaction analysis, and disease modeling. 

These real-world applications show why graph neural networks are valuable wherever data is highly connected, and relationships drive outcomes. 

Challenges and Limitations of Graph Neural Networks 

Graph neural networks are powerful, but they come with practical challenges. Understanding these limits helps you use them effectively and avoid common pitfalls. 

Below is the table explaining the major challenges: 

Challenge	Description
Scalability	Large graphs with millions of nodes increase memory use and slow training due to repeated message passing.
Over-smoothing	Too many layers cause node representations to become similar, reducing the model’s ability to distinguish nodes.
Computational cost	Irregular graph structures make computation slower and harder to optimize on GPUs.
Graph quality dependency	Missing or noisy edges reduce learning effectiveness and can mislead the model.
Limited interpretability	Explaining why a prediction was made is difficult because influence across nodes is hard to trace.
Generalization issues	Some models struggle to adapt to new or dynamic graphs without retraining them.

Also Read: Top 20 Challenges of Artificial Intelligence 

Conclusion 

Graph neural networks offer a powerful way to learn from connected data by modeling relationships directly. They work well in areas where structure and interaction matter more than isolated values. By understanding their architecture, use cases, and limits, you can decide when graph neural networks are the right choice for solving real-world problems. 

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