Hyperbolic embedding of higher-order simplicial networks

Abstract

Many biological interactions involve groups rather than pairs, as when several proteins assemble into a multi-protein complex. Simplicial complexes naturally capture these collective interactions. Existing hyperbolic embeddings represent network hierarchy and similarity through a few interpretable coordinates, but rely only on pairwise links: they cannot distinguish three units engaged in a common interaction from three units connected only in pairs. We introduce here the hyperbolic higher-order (HHO) embedding, which embeds a simplicial complex into the Poincaré disk, while incorporating its group interactions. The hyperbolic area of the triangle spanned by three interacting nodes then measures how compactly their interaction is represented. Testson synthetic and real-world networks, including a co-authorship network and the protein interaction network of \textit{Escherichia coli}, show that HHO reduces the hyperbolic area of triangles representing simplices relative to those obtained from pairwise network embeddings. HHO also recovers node groups more accurately than pairwise embeddings and a weighted-clique alternative, with performance competitive with Euclidean embeddings, while providing a low-dimensional representation that can be analyzed with the tools of network geometry.