Intro To Graph Neural Networks || Kefei Hu

A graph is a data structure consisting of two components, nodes and edges. It is useful for modelling relationships and interactions between interconnected entities. Many types of data can naturally be represented this way, such as social networks, molecule interactions or even websites. Leveraging the relational structure gives us the potential to build more accurate models for classification, prediction and clustering. This talk introduces Graph Neural Networks (GNNs), a class of models designed to perform inference on such interconnected data. They are well suited for a wide range of applications from drug discovery to recommender systems and traffic prediction. We will cover the theory of GNNs, specifically how they work and scale to large graphs, as well as a small demo of a GNN for entity classification using Stellar Graph. PUBLICATION PERMISSIONS:
PyData provided Coding Tech with the permission to republish PyData Talks. CREDITS:
PyData YouTube channel: https://www.youtube.com/channel/UCOjD18EJYcsBog4IozkF_7w https://www.youtube.com/watch?v=L7-MkgS-ue4