• Understand the foundations of graph theory and representation learning.

• Implement Graph Convolutional Networks (GCNs).

• Explore Graph Attention Networks (GATs) and GraphSAGE.

• Use frameworks like PyTorch Geometric and DGL.

• Perform node classification and link-prediction tasks.

• Build and train GNNs on real-world graph datasets.

• Optimize model performance and scalability.

• Apply GNNs in social, chemical, and financial networks.

• Interpret graph embeddings and visualization outputs.

• Prepare for advanced AI research or data-science roles involving graph data.

Course Syllabus

Module 1: Introduction to Graph Theory and Graph Data
Module 2: Graph Representations and Adjacency Matrices
Module 3: Fundamentals of Graph Neural Networks
Module 4: Graph Convolutional Networks (GCN)
Module 5: Graph Attention Networks (GAT) and GraphSAGE
Module 6: Frameworks – PyTorch Geometric and DGL
Module 7: Training and Optimization of GNN Models
Module 8: Applications – Recommenders, Molecules, and Fraud Detection
Module 9: Scaling GNNs for Large Graphs and Distributed Environments
Module 10: Capstone Project – End-to-End Graph Learning Pipeline

Upon successful completion, learners receive a Certificate of Completion from Uplatz, confirming expertise in Graph Neural Networks (GNNs). This Uplatz certification validates your ability to design, train, and deploy deep-learning models on graph-structured data.

The certification aligns with real-world applications in AI research, data science, and enterprise analytics where relationship-driven insights are vital. It is ideal for professionals seeking roles in machine learning, network science, and data-driven product engineering.

This credential demonstrates your capacity to apply deep-learning techniques beyond traditional data formats, preparing you for cutting-edge roles in research and industry.

With increasing demand for intelligent relationship modeling, Graph Neural Network specialists are among the most sought-after AI professionals today.

After completing this course with Uplatz, you can pursue roles such as:

• Graph Machine Learning Engineer

• AI Research Scientist (GNN Specialist)

• Data Scientist – Knowledge Graph Analytics

• Recommender System Engineer

• Fraud Detection AI Analyst

Professionals in this domain earn between $110 000 and $200 000 per year, depending on experience and project complexity.

Career opportunities exist in tech giants, fintech startups, biotech firms, and research labs applying relational deep learning to solve complex, interconnected problems. With this course, you’ll be prepared to engineer the next generation of graph-intelligent systems powering modern AI ecosystems.

1. What is a Graph Neural Network?
   A deep-learning model that operates on graph-structured data to learn representations of nodes and edges.

2. How does a GCN differ from a CNN?
   GCNs apply convolutions to irregular graph data structures, while CNNs work on grid-like data such as images.

3. What is message passing in GNNs?
   The process by which nodes aggregate information from their neighbors to update their representations.

4. What are Graph Embeddings?
   Vector representations of nodes or entire graphs that capture relational patterns for downstream tasks.

5. What are Graph Attention Networks (GATs)?
   GNN variants that assign attention weights to neighbor nodes during message aggregation.

6. What is GraphSAGE?
   A sampling-based approach that enables GNNs to scale to very large graphs by aggregating sampled neighbors.

7. What are common applications of GNNs?
   Social network analysis, fraud detection, drug discovery, and recommendation systems.

8. Which frameworks are used to build GNNs?
   PyTorch Geometric, Deep Graph Library (DGL), and GraphX.

9. What are challenges in training GNNs?
   Over-smoothing, high computational cost, and memory limitations for large graphs.

10. How can GNN models be optimized for large datasets?
    Through sampling techniques, mini-batch training, and distributed computation.