Weisfeiler and Lehman Go Categorical

TL;DR

This paper introduces a categorical framework for hypergraph neural networks, deriving new architectures with enhanced expressivity and validating their effectiveness through theoretical analysis and experiments.

Contribution

It formalizes hypergraph neural networks using category theory, deriving new architectures with proven expressive power and demonstrating their advantages empirically.

Findings

The proposed models subsume the expressive power of the standard Hypergraph Weisfeiler-Lehman test.

The incidence-based architecture captures complex intersection geometries.

Experimental results validate the theoretical expressivity improvements.

Abstract

While lifting map has significantly enhanced the expressivity of graph neural networks, extending this paradigm to hypergraphs remains fragmented. To address this, we introduce the categorical Weisfeiler-Lehman framework, which formalizes lifting as a functorial mapping from an arbitrary data category to the unifying category of graded posets. When applied to hypergraphs, this perspective allows us to systematically derive Hypergraph Isomorphism Networks, a family of neural architectures where the message passing topology is strictly determined by the choice of functor. We introduce two distinct functors from the category of hypergraphs: an incidence functor and a symmetric simplicial complex functor. While the incidence architecture structurally mirrors standard bipartite schemes, our functorial derivation enforces a richer information flow over the resulting poset, capturing complex intersection geometries often missed by existing methods. We theoretically characterize the expressivity of these models, proving that both the incidence-based and symmetric simplicial approaches subsume the expressive power of the standard Hypergraph Weisfeiler-Lehman test. Extensive experiments on real-world benchmarks validate these theoretical findings.