Machine Learning on Dynamic Graphs - Temporal Graph Networks

Explore the cutting-edge field of machine learning on dynamic graphs in this 45-minute conference talk from MLOps World: Machine Learning in Production. Delve into the world of Graph Neural Networks (GNNs) and their applications in various domains such as biology, chemistry, social science, and physics. Learn about the limitations of traditional GNN models designed for static graphs and discover the importance of analyzing dynamic, time-evolving graphs in real-world scenarios like social networks, financial transactions, and recommender systems. Gain insights into Temporal Graph Networks, a novel approach for machine learning on dynamic graphs that captures crucial temporal information. Led by Emanuele Rossi, a Machine Learning Researcher at Twitter and PhD student at Imperial College, this talk offers a comprehensive overview of the latest advancements in graph-based machine learning techniques for evolving network structures.

Machine Learning on Dynamic Graphs