Generalized Simplicial Attention Neural Networks

Understand

Graph machine learning methods excel at leveraging pairwise relations present in the data.

• However, graphs are unable to fully capture the multi-way interactions inherent in many complex systems.
• An effective way to incorporate them is to model the data on higher-order combinatorial topological spaces, such as Simplicial Complexes (SCs) or Cell Complexes.
• For this reason, we introduce Generalized Simplicial Attention Neural Networks (GSANs), novel neural network architectures designed to process data living on simplicial complexes using masked self-attentional layers.