Paper on community detection in hypergraphs published in Science Advances

Together with Jiaze Li and Leto Peel (both Maastricht University), we have a new paper in Science Advances [1]. Our results outline that there are necessary trade-offs for detecting communities in hypergraphs.

Among other things we derive a Bethe Hessian operator for nonuniform hypergraphs that provides efficient spectral clustering with principled model selection. We characterize the resulting spectral detectability threshold and compare it to belief propagation limits, showing the methods coincide for uniform hypergraphs but diverge in nonuniform settings.

[1] Jiaze Li et al. ,Higher-order trade-offs in hypergraph community detection.Sci. Adv.12,eaef2184(2026). DOI:10.1126/sciadv.aef2184