Giuseppe Futia, CSI - Piedmontese Consortium for Information Systems (Italy), Data Engineer

Graphs provide a powerful framework for modeling relationships between entities, making Graph Neural Networks (GNNs) a crucial tool for applying machine learning to graph-structured data. However, implementing GNNs can be challenging due to the complexities of integrating graph structures with deep learning techniques.

This hands-on tutorial is designed for data scientists and machine learning engineers who are proficient in Python and have a foundational understanding of deep learning. Participants will gain practical experience building complete GNN workflows, from data preprocessing to model training and evaluation. Additionally, we will explore advanced techniques that combine GNNs with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), enabling innovative "chat-with-your-graph" interactions for enhanced data exploration.

By the end of this tutorial, participants will have the expertise and tools to effectively incorporate GNNs into their own projects.