Make Any Graph Neural Network Go Topological with TopoTune

Graph Neural Networks (GNNs) excel at learning from relational data, but real-world systems—like biological or social networks—involve complex multiway interactions beyond simple pairwise relationships. The emerging field of Topological Deep Learning (TDL) captures these higher-order structures, yet lacks the standardized tools that made GNNs so accessible.

In this talk, I will introduce TopoTune: a lightweight framework that lets practitioners build and train powerful TDL models using any existing GNN—with unprecedented ease. Theoretical results show TopoTune generalizes the entire landscape of traditional TDL models, while experiments demonstrate it consistently matches or outperforms prior models, often with less complexity. I will also showcase new research in the community that leverages TopoTune for drastic reductions in computational cost as well as new scientific applications.