TopoU-Net: a U-Net architecture for topological domains

TL;DR

TopoU-Net introduces a topological domain-aware U-Net architecture that leverages combinatorial complexes and rank-paths to effectively process higher-order structured data, outperforming traditional methods.

Contribution

It presents a novel rank-path U-Net design for topological domains, replacing spatial scales with rank-based paths, and clarifies the role of skip connections through bottleneck support ratios.

Findings

Achieves the strongest mean accuracy on 6 of 8 node-classification datasets.

Outperforms baselines on 4 of 5 hypergraph datasets.

Removing skip connections harms performance under severe bottleneck compression.

Abstract

Many modern datasets mix points, edges, regions, groups, objects, events, hyperedges, and relations. Yet neural architectures often force such data into grids, graphs, or sequences, obscuring higher-order structure and making encoder-decoder designs domain-specific. We view U-Net not as a grid-specific architecture, but as a hierarchical encoder-decoder principle: representation spaces, transport maps between levels, and skip connections between matched levels. Combinatorial complexes naturally supply these ingredients through cells, incidences, and ranks. We introduce TopoU-Net, a rank-path U-Net for topological domains. Given a path from an input rank to a bottleneck rank and back, the encoder lifts cochains upward along incidence maps, the decoder transports them downward, and skip connections merge features at matched ranks. Rank replaces spatial scale: choosing paths through nodes, edges, faces, hyperedges, or global cells becomes the central architectural decision. A key quantity is the bottleneck support ratio, the number of cells at the bottleneck relative to the number of cells at the input rank. This ratio is fixed by the complex and chosen path rather than by arbitrary pooling, and it clarifies when skip connections are optional, useful, or structurally important. Across node classification, graph classification, hypergraph node classification, mesh classification, and image reconstruction, TopoU-Net provides a reusable encoder-decoder template for higher-order structured data. Among the evaluated baselines, it achieves the strongest mean accuracy on six of eight node-classification datasets and four of five hypergraph datasets, with the largest gains on heterophilic graphs. Ablations show that removing skip connections is most damaging under severe bottleneck compression.