Graph Neural Networks (gnns) Training Course: Network Data Modeling & Analysis

Introduction

Unlock the power of relational data with our Graph Neural Networks (GNNs) Training Course. This program is designed to equip you with the essential skills to analyze and model network data, enabling you to build powerful applications that leverage the interconnected nature of data. In today's data-driven world, mastering GNNs is crucial for developing innovative solutions in various fields, from social network analysis to drug discovery. Our GNNs training course offers hands-on experience and expert guidance, empowering you to implement state-of-the-art graph-based models.

This network data modeling training delves into the core concepts of graph neural networks, covering topics such as graph convolutions, message passing, and node and graph embedding. You'll gain expertise in using industry-standard libraries and tools to analyze and model network data, meeting the demands of modern graph-based AI projects. Whether you're a data scientist, AI developer, or researcher, this Graph Neural Networks (GNNs) course will empower you to build powerful graph-based models.

Target Audience:

• Data Scientists
• AI Developers
• Machine Learning Engineers
• Researchers
• Network Analysts
• Bioinformatics Specialists
• Anyone needing GNNs skills

Course Objectives:

• Understand the fundamentals of Graph Neural Networks (GNNs).
• Master graph convolution networks (GCNs) for node classification.
• Utilize graph attention networks (GATs) for relational learning.
• Implement graph embedding techniques for network analysis.
• Design and build GNN models for various network data applications.
• Optimize GNN models for performance and scalability.
• Troubleshoot and address common GNN implementation challenges.
• Implement model evaluation and validation techniques for GNNs.
• Integrate GNN models into real-world systems.
• Understand how to handle large-scale graph data.
• Explore advanced GNN architectures (e.g., GraphSAGE, RGCN).
• Apply real world use cases for GNNs in various domains.
• Leverage GNN libraries for efficient model implementation.

Duration

10 Days

Course content

Module 1: Introduction to Graph Neural Networks (GNNs)

• Fundamentals of Graph Neural Networks (GNNs).
• Overview of graph convolutions, message passing, and graph embeddings.
• Setting up a GNNs development environment.
• Introduction to GNNs libraries and tools.
• Best practices for GNNs.

Module 2: Graph Convolution Networks (GCNs)

• Implementing GCNs for node classification tasks.
• Utilizing spectral and spatial graph convolutions.
• Designing and building GCN models for graph data.
• Optimizing GCNs for node-level predictions.
• Best practices for GCNs.

Module 3: Graph Attention Networks (GATs)

• Implementing GATs for relational learning.
• Utilizing attention mechanisms for node interactions.
• Designing and building GAT models for graph data.
• Optimizing GATs for edge-level predictions.
• Best practices for GATs.

Module 4: Graph Embedding Techniques

• Implementing graph embedding techniques (Node2Vec, DeepWalk).
• Utilizing graph embeddings for node and graph representations.
• Designing and building embedding models for network analysis.
• Optimizing graph embeddings for downstream tasks.
• Best practices for graph embeddings.

Module 5: GNN Model Design

• Designing GNN models for specific network data applications.
• Implementing model architectures for various graph tasks.
• Utilizing graph data preprocessing techniques.
• Optimizing model design for graph data.
• Best practices for GNN model design.

Module 6: Model Optimization and Scalability

• Optimizing GNN models for performance and scalability.
• Utilizing batching and sampling techniques for large graphs.
• Implementing distributed GNN training.
• Designing scalable GNN solutions.
• Best practices for model optimization.

Module 7: Troubleshooting GNN Implementation Challenges

• Debugging common GNN implementation issues.
• Analyzing model performance and stability.
• Utilizing troubleshooting techniques for model improvement.
• Resolving common GNN challenges.
• Best practices for troubleshooting.

Module 8: Model Evaluation and Validation for GNNs

• Implementing evaluation metrics for GNN tasks.
• Utilizing cross-validation techniques for graph data.
• Designing and building model validation pipelines.
• Optimizing model evaluation strategies.
• Best practices for model evaluation.

Module 9: Integration with Real-World Systems

• Integrating GNN models into real-world applications.
• Utilizing APIs and deployment tools for GNNs.
• Implementing real-time graph-based systems.
• Optimizing models for deployment environments.
• Best practices for integration.

Module 10: Handling Large-Scale Graph Data

• Implementing techniques for handling large-scale graph data.
• Utilizing graph partitioning and distributed processing.
• Designing and building scalable graph processing pipelines.
• Optimizing data handling for large graphs.
• Best practices for large graphs.

Module 11: Advanced GNN Architectures

• Implementing GraphSAGE for inductive learning.
• Utilizing Relational Graph Convolutional Networks (RGCNs).
• Designing and building advanced GNN models.
• Optimizing advanced architectures for specific tasks.
• Best practices for advanced architectures.

Module 12: Real-World Use Cases

• Implementing GNNs for social network analysis.
• Utilizing GNNs for drug discovery and bioinformatics.
• Implementing GNNs for recommendation systems.
• Utilizing GNNs for fraud detection.
• Best practices for real-world applications.

Module 13: GNN Libraries Implementation

• Utilizing PyTorch Geometric for GNN implementation.
• Implementing GNN models with Deep Graph Library (DGL).
• Designing and building GNN pipelines with libraries.
• Optimizing library usage for efficient implementation.
• Best practices for library implementation.

Module 14: Model Interpretability for GNNs

• Implementing model interpretability techniques for GNNs.
• Utilizing visualization tools for graph-based explanations.
• Designing and building interpretable GNN models.
• Optimizing model transparency.
• Best practices for model interpretability.

Module 15: Future Trends in GNNs

• Emerging trends in graph neural networks.
• Utilizing transformer-based GNNs.
• Implementing GNNs for dynamic graphs and temporal networks.
• Best practices for future GNN applications.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.