Ekaterina Sirazitdinova

Graph Neural Networks: What’s behind the Hype?

How does AI predict traffic, recommend friends, and discover new drugs? Learn how Graph Neural Networks derive insights from complex, unstructured data.

Graph Neural Networks: What’s behind the Hype?
#1about 2 minutes

Why graph neural networks excel with unstructured data

Graph neural networks are uniquely suited for unstructured data like 3D meshes and social networks where traditional CNNs struggle.

#2about 3 minutes

Reviewing core concepts from graph theory

Key graph theory concepts are explained, including nodes, edges, directed vs undirected graphs, and homogeneous vs heterogeneous graphs.

#3about 2 minutes

Choosing the right data structure for graphs

An adjacency matrix is suitable for small graphs, while an adjacency list is more spatially efficient for large, sparse graphs.

#4about 3 minutes

A brief refresher on deep learning fundamentals

The core deep learning process of training and inference is reviewed, along with the distinction between supervised and unsupervised learning.

#5about 6 minutes

Exploring graph, node, and edge level prediction tasks

GNNs can perform predictions at the graph level (molecule properties), node level (community detection), and edge level (recommendation systems).

#6about 4 minutes

Understanding the GNN training and data splitting process

GNNs are trained using the message passing algorithm to create node embeddings, followed by a transductive split for training and validation sets.

#7about 2 minutes

Frameworks and resources for building GNNs

Popular frameworks like DGL, PyTorch Geometric, and TensorFlow GNN simplify the implementation of graph neural networks.

#8about 1 minute

Summary of key concepts in graph neural networks

The talk concludes with a recap of key takeaways, including graph modeling, data representation, prediction tasks, and the message passing algorithm.

#9about 3 minutes

Q&A on data leakage, knowledge graphs, and embeddings

The Q&A session addresses audience questions about data leakage in transductive splits, applying GNNs to semantic knowledge graphs, and comparing graph embeddings to word embeddings.

Related jobs
Jobs that call for the skills explored in this talk.

Machine Learning Engineer

Picnic Technologies B.V.

Picnic Technologies B.V.
Amsterdam, Netherlands

Intermediate
Senior
Python
Structured Query Language (SQL)
+1

Matching moments

Exploring the role and ethics of AI in gaming
14:06 MIN

Exploring the role and ethics of AI in gaming

Devs vs. Marketers, COBOL and Copilot, Make Live Coding Easy and more - The Best of LIVE 2025 - Part 3

Shifting from traditional CVs to skill-based talent management
05:17 MIN

Shifting from traditional CVs to skill-based talent management

From Data Keeper to Culture Shaper: The Evolution of HR Across Growth Stages

Building an open source community around AI models
04:28 MIN

Building an open source community around AI models

AI in the Open and in Browsers - Tarek Ziadé

The evolving role of the machine learning engineer
02:20 MIN

The evolving role of the machine learning engineer

AI in the Open and in Browsers - Tarek Ziadé

Playing a game of real or fake tech headlines
04:17 MIN

Playing a game of real or fake tech headlines

WeAreDevelopers LIVE – You Don’t Need JavaScript, Modern CSS and More

Why specialized models outperform generalist LLMs
05:09 MIN

Why specialized models outperform generalist LLMs

AI in the Open and in Browsers - Tarek Ziadé

The hardware requirements for running LLMs locally
03:55 MIN

The hardware requirements for running LLMs locally

AI in the Open and in Browsers - Tarek Ziadé

How Python became the dominant language for AI
04:09 MIN

How Python became the dominant language for AI

AI in the Open and in Browsers - Tarek Ziadé

Featured Partners

Related Videos

Graphs and RAGs Everywhere... But What Are They? - Andreas Kollegger - Neo4j46:51

Graphs and RAGs Everywhere... But What Are They? - Andreas Kollegger - Neo4j

Knowledge graph based chatbot24:44

Knowledge graph based chatbot

Tomaz Bratanic

Large Language Models ❤️ Knowledge Graphs29:11

Large Language Models ❤️ Knowledge Graphs

Michael Hunger

Martin O'Hanlon - Make LLMs make sense with GraphRAG24:42

Martin O'Hanlon - Make LLMs make sense with GraphRAG

Martin O'Hanlon

Trends, Challenges and Best Practices for AI at the Edge29:50

Trends, Challenges and Best Practices for AI at the Edge

Ekaterina Sirazitdinova

Multimodal Generative AI Demystified56:55

Multimodal Generative AI Demystified

Ekaterina Sirazitdinova

Geometric deep learning for drug discovery42:56

Geometric deep learning for drug discovery

Noah Weber

WWC24 - Ankit Patel - Unlocking the Future Breakthrough Application Performance and Capabilities with NVIDIA22:07

WWC24 - Ankit Patel - Unlocking the Future Breakthrough Application Performance and Capabilities with NVIDIA

Ankit Patel

Related Articles

View all articles
DN
Dr. Andy R. Terrel - NVIDIA
What’s the latest in NVIDIA CUDA Python
Python and NVIDIA CUDA have long been friends. Over the last year, NVIDIA teams are working to improve the Pythonista’s experience. This means a top-to-bottom update to the CUDA Platform is fueling the GenAI movement, e.g. llama3, gpt and nemo. These...
What’s the latest in NVIDIA CUDA Python
MH
Michael Hunger, Neo4j
Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?
After a few intense years of building with AI, many teams are asking the same question: Where is the ROI? We’re shipping chatbots, copilots, and agents . But: Agents aren’t autonomous enough to own end‑to‑end workflows. Model quality degrades over t...
Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?
DC
Daniel Cranney
How to Use Generative AI to Accelerate Learning to Code
It’s undeniable that generative-AI and LLMs have transformed how developers work. Hours of hunting Stack Overflow can be avoided by asking your AI-code assistant, multi-file context can be fed to the AI from inside your IDE, and applications can be b...
How to Use Generative AI to Accelerate Learning to Code

From learning to earning

Jobs that call for the skills explored in this talk.