Simplicial Network

Simplicial networks represent data as higher-order structures (simplicial complexes) extending beyond simple graphs to capture multi-way relationships. Current research focuses on developing novel neural network architectures, such as directed simplicial neural networks and Hodge-Laplacian based models, to process and learn from this richer data representation, often employing message-passing or alternative approaches like MLPs. This approach offers improved expressiveness and robustness compared to traditional graph-based methods, with applications spanning diverse fields including image processing, signal processing, and network analysis.