Theoretical Foundations of Efficient and Scalable Graph Learning

This project aims to develop mathematical foundations for understanding and improving graph neural networks (GNNs), which are widely used machine learning models for data with graph structures. Such data arises in recommender systems, molecular modeling and a range of scientific and technological domains. While GNNs have achieved notable empirical…