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Understanding Real-World Applications of Graph Neural Networks | EP.17

Join Ron Green in episode 17 as he delves into the world of artificial intelligence with industry veterans Paco Nathan and Dr. Steve Kramer.

In this episode, they explore the rise of graph neural networks (GNNs), a game-changer in AI for modeling complex relationships across various domains. From weather forecasting to fraud detection, GNNs are revolutionizing how we leverage connected data.

Discover how GNNs reduce computation time, tackle the cold start problem in recommendation engines, and even aid in election integrity. Learn about real-world applications in pharma, healthcare, and cybersecurity, and the promising future of hybrid AI architectures merging symbolic and neural approaches.


“Introduction to Graph Machine Learning”
Clémentine Fourrier

Hugging Face (2023-01-03)

“Inductive Representation Learning on Large Graphs”
William Hamilton, Rex Ying, Jure Leskovec
NeurIPS (2017–06–17)

Graph Representation Learning
William Hamilton
Morgan and Claypool (2020)

Open source for GraphSAGE algorithm

“Getting Started with Graph Embeddings in Neo4j”
CJ Sullivan
Towards Data Science (2021–05–23)

“GraphCast: Learning skillful medium-range global weather forecasting”Remi Lam, et al. (2023-08-04)

“The Future of Graph Databases”
The Data Exchange podcast (2023–07–23)
Episode 190: Ben Lorica interviews Emil Eifrem @ Neo4j

“Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks”
Marco De Nadai, et al.
ACM (2024–03–08)

“Graph Reasoning Reading List on HuggingFace” 

Paco Nathan

“Graphs and Language”
Louis Guitton

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