The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks

TL;DR

This paper introduces a hierarchy for hypergraph neural networks based on their ability to detect and count small structural patterns, defining a fundamental architectural limit called the Width Wall.

Contribution

It formalizes hypergraph expressivity using homomorphism densities, establishing a strict hierarchy and identifying the Width Wall as a key architectural boundary.

Findings

Homomorphism densities generate all continuous hypergraph invariants.

The Width Wall predicts when graph-reduction methods fail.

Density features extend expressivity beyond bounded-width message passing.

Abstract

Hypergraphs provide a natural framework to model higher-order interactions in scientific, social, and biological systems. Hypergraph neural networks (HGNNs) aim to learn from such data, yet it remains unclear which higher-order structures these models can represent. We show that hypergraph expressivity is governed by which small patterns an architecture can detect and count. We formalize this via homomorphism densities, which measure how often a structural motif appears in a hypergraph. Combining classical homomorphism-count completeness with invariant approximation, we show that homomorphism densities generate all continuous hypergraph invariants and organize them into a strict hierarchy indexed by hypertree width. This yields a Width Wall: a fundamental architectural limit beyond which no hidden dimension, training procedure or fixed-depth HGNN can represent invariants requiring wider patterns. Our framework provides a unified characterization of 15 HGNN architectures, precisely identifies information lost by clique expansion, and motivates density-aware models that extend expressivity beyond bounded-width message passing. We experimentally validate this finding on an APPLICATION NODE CLASSIFICATION SUITE of real-world hypergraphs, where the Width Wall predicts when graph-reduction baselines fail and when density features help.