Stanford Graph Learning Workshop 2022

Advances in Academia, Industry and the PyG Graph Learning Framework

Overview

Over the past few years, graphs have emerged as one of the most important and useful abstractions for representing complex data, including social networks, knowledge graphs, financial transactions / purchasing behavior, supply chain networks, molecular graphs, biomedical networks, as well as for modeling 3D objects, manifolds, and source code. Deep representation learning on graphs is an emerging field with a wide array of applications, ranging from protein folding and fraud detection, to drug discovery and recommender systems.

In the Stanford Graph Learning Workshop, we will bring together thought leaders from academia and industry to showcase the most cutting edge and recent methodological advances in Graph Neural Networks. The workshop will present new developments in the leading graph machine learning framework and a wide range of graph machine learning applications to different domains. Additionally, the workshop will discuss practical challenges for large-scale training and deployment of graph-based machine learning models.

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Agenda