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Graph Neural Networks (GNN) have been rapidly advancing and have recently become the dark horse behind many exciting achievements, from research breakthroughs to actual solutions with large-scale deployment. But why do Graph Neural Networks (GNN) matter in 2024?

This video will recap some impactful applications of GNNs in science and industry, and the most recent research highlights, focusing on applied use cases. With the latest applications of GNNs spanning from Relational Databases, Materials Science, Dynamical System Modeling, to the most advanced Recommendation Systems out there, we take an overview of diverse application areas unlocked by Graph Machine Learning (Graph ML).

For all references made in the video, see below.

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▬▬▬▬ REFERENCES AND SOURCES ▬▬▬▬
Uber Eats (Uber) - https://www.uber.com/en-GR/blog/uber-...
Pinsage (Pinterest) - https://arxiv.org/abs/1806.01973
Google Maps (DeepMind) - https://deepmind.google/discover/blog...
GraphCast (DeepMind) - https://deepmind.google/discover/blog...
Relational Databases (Stanford/KumoAI/MPII) - https://relbench.stanford.edu/paper.pdf
GNoME (DeepMind) - https://deepmind.google/discover/blog...
Antibiotic Discovery / Halicin (MIT) - https://www.sciencedirect.com/science...
Explainable GNNs (MIT/Harvard) - https://www.nature.com/articles/s4158...
RFdiffusion (Baker Lab) - https://www.bakerlab.org/2023/07/11/d...
Chroma (Generate:Biomedicines) - https://github.com/generatebio/chroma
Paper on Non-Euclidean Structure Data - https://www.mdpi.com/2073-8994/15/4/837

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0:00 Intro
0:44 What are Graph Neural Networks?
1:45 Recommendation Systems
3:19 Traffic Prediction
3:53 Weather Prediction
4:44 Data Mining
6:35 Materials Science
8:02 Drug Discovery
10:05 Protein Design
11:41 Final Words

#machinelearning #deeplearning #ai