Lecture
This lecture discusses the choice of graph neural network architectures, focusing on the prediction of effective methods and relevant benchmarks. It provides an overview of various architectures, from low to high complexity models, and explores the evaluation of graph networks in image and graph classification tasks. The lecture also delves into experiments on model complexity and performance, highlighting the importance of data statistics in selecting the appropriate model. Insights from supervised learning are used to guide the discussion, emphasizing the impact of the number of observed nodes and feature dimensionality on model optimization.