Graph Neural Networks

Series Overview

This series examines representation learning on structured data through graph neural networks. We explore how message passing, spectral methods, and topological insights combine to enable powerful learning on graphs.

Key Topics Covered:

• Message passing neural networks and aggregation schemes
• Spectral graph theory and graph convolutions
• Expressiveness and the Weisfeiler-Leman hierarchy
• Graph attention mechanisms and transformers
• Applications to molecules, social networks, and knowledge graphs

Structured Learning

Graphs provide a natural representation for relational data, and GNNs offer a principled way to learn from this structure. By understanding the theoretical foundations and practical considerations, we can build models that respect and exploit the geometry of our data.

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Posts in this Series