Welcome to the latest post in the Graphs for Data Science substack. In this post, we explore the fundamentals of Graph Neural Networks using a fascinating dataset on molecular structures.
As always, you can find the companion notebook on the Graphs for Data Science GitHub repository:
In this post, we’re flipping our usual script, and instead of focusing on analyzing a graph, we’re going to learn from one. We’ll build a neural network to predict a molecule's solubility in water purely from the graph structure of the connections between atoms. The family of models that pulls this off goes by the name Graph Neural Networks (GNNs), and they’ve quietly become one of the most versatile tools in the modern ML toolkit.
For the sake of convenience (and a shiny new dataset), we’re using molecules as our playground. Still, the core ideas: message passing, neighborhood aggregation, and graph-level readout are applicable anywhere you encounter graph structure: social networks, road systems, knowledge…
