TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks

Understand

Graph Neural Networks (GNNs) effectively learn from relational data by leveraging graph symmetries.

• However, many real-world systems -- such as biological or social networks -- feature multi-way interactions that GNNs fail to capture.
• Topological Deep Learning (TDL) addresses this by modeling and leveraging higher-order structures, with Combinatorial Complex Neural Networks (CCNNs) offering a general and expressive approach that has been shown to outperform GNNs.
• However, TDL lacks the principled and standardized frameworks that underpin GNN development, restricting its accessibility and applicability.