The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks | Paper Citation Record

Observation e2c8a2d0-c9b1-48ff-bb15-f610bf2ab4f4 · outbound

This paper cites Absil, Robert Mahony, and Rodolphe Sepulchre.Optimization Algorithms on Matrix Manifolds.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Absil, Robert Mahony, and Rodolphe Sepulchre.Optimization Algorithms on Matrix Manifolds

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.832823Z

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Observation 3c0be1cd-c2a0-48dc-ae26-847cf202ee7f · outbound

This paper cites Ranking via Sinkhorn Propagation.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Ranking via Sinkhorn Propagation

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-14T20:42:58.738888Z

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Observation eee83398-8c23-49a3-b16a-4dae23f974d1 · outbound

This paper cites HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:42:58.753185Z

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Observation 56b56068-4a31-471c-9689-8b38de43c11c · outbound

This paper cites Parameter-free hypergraph neural network for few-shot node classification.Advances in Neural Information Processing Systems (NeurIPS).

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Parameter-free hypergraph neural network for few-shot node classification.Advances in Neural Information Processing Systems (NeurIPS)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.837089Z

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Observation 364b4d40-ed1b-4835-86bd-304cf84151b8 · outbound

This paper cites Bartlett, Dylan J.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Bartlett, Dylan J

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.840708Z

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Observation 5564e1c2-8d4b-4b19-b26c-cf3899e75af3 · outbound

This paper cites Networks beyond pairwise interactions: Structure and dynamics.Physics Reports, 874:1–92.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Networks beyond pairwise interactions: Structure and dynamics.Physics Reports, 874:1–92

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.827901Z

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Observation ba6582d4-c897-4ce3-868e-5e4af961c0e4 · outbound

This paper cites Benson, David F.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Benson, David F

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.806902Z

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Observation 7a725f9b-c585-4a71-8a73-5aef250938dc · outbound

This paper cites Weisfeiler and Lehman go topological: Message passing simplicial networks.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Weisfeiler and Lehman go topological: Message passing simplicial networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.770855Z

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Observation 41bd0d8c-aff9-4eb0-b42f-69120aeab44b · outbound

This paper cites Color refinement, homomorphisms, and hypergraphs.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Color refinement, homomorphisms, and hypergraphs

Reference 9

Resolution: malformed identifier; raw_fallback, observed 2026-05-15T15:56:14.775461Z

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Observation 1ef1dcea-7412-4cc6-9b21-f46d0f7ccdab · outbound

This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-05-14T20:42:58.771502Z

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Observation e6bec948-9b45-4235-b384-7afa7052d765 · outbound

This paper cites An optimal lower bound on the number of variables for graph identification.Combinatorica, 12(4):389–410.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks An optimal lower bound on the number of variables for graph identification.Combinatorica, 12(4):389–410

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.798301Z

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Observation 6f5153de-e0f0-48a1-a285-9fc084fc306a · outbound

This paper cites A recursive theta body for hypergraphs.Combinatorica, 43:909–938.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks A recursive theta body for hypergraphs.Combinatorica, 43:909–938

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.702808Z

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Observation 465e0d0b-02f9-4496-b6b9-3a9ac2e8ec16 · outbound

This paper cites Hubert Chan, Anand Louis, Zhihao Gavin Tang, and Chenzi Zhang.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Hubert Chan, Anand Louis, Zhihao Gavin Tang, and Chenzi Zhang

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.706390Z

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Observation 941626c3-e614-4cae-9447-5d4d516cd413 · outbound

This paper cites You are AllSet: A multiset function framework for hypergraph neural networks.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks You are AllSet: A multiset function framework for hypergraph neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.814947Z

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Observation 14d9c8e0-0e24-477c-ab10-799cda43f2a3 · outbound

This paper cites Weisfeiler and Lehman go categorical.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Weisfeiler and Lehman go categorical

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:42:58.759736Z

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Observation 3b6df2d1-35e2-424d-ad99-86fb3ba42d0b · outbound

This paper cites Colbourn and Jeffrey H.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Colbourn and Jeffrey H

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.820051Z

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Observation 465a53e5-cacb-4cca-99b4-0a8a85aa7457 · outbound

This paper cites All convex invariant functions of Hermitian matrices.Archiv der Mathematik, 8:276–278.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks All convex invariant functions of Hermitian matrices.Archiv der Mathematik, 8:276–278

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.824212Z

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Observation 1494f063-b26f-401c-a48d-ab7f0158e22a · outbound

This paper cites Exploiting group symmetry in semidefinite programming relaxations of the quadratic assignment problem.Mathematical Programming, 122:225–246.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Exploiting group symmetry in semidefinite programming relaxations of the quadratic assignment problem.Mathematical Programming, 122:225–246

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.676709Z

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Observation e186591d-fae1-492a-91b3-036a9f73710b · outbound

This paper cites Lovász meets Weisfeiler and Leman.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Lovász meets Weisfeiler and Leman

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.680020Z

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Observation 33e42499-c815-451f-97db-c062b46e7e3e · outbound

This paper cites HNHN: Hypergraph Networks with Hyperedge Neurons.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks HNHN: Hypergraph Networks with Hyperedge Neurons

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:42:58.766070Z

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Observation 33f3443d-21fd-498a-84c2-32aa9061e6b0 · outbound

This paper cites Sheaf hypergraph networks.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Sheaf hypergraph networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.714744Z

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Observation 9eba217c-6f81-45a3-b63c-914edf5aebee · outbound

This paper cites A measure-theoretic approach to the theory of dense hyper- graphs.Advances in Mathematics, 231(3–4):1731–1772.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks A measure-theoretic approach to the theory of dense hyper- graphs.Advances in Mathematics, 231(3–4):1731–1772

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.597143Z

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Observation 07cc90e0-19e4-457f-83e7-95853d5ae84b · outbound

This paper cites Hypergraph neural networks.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Hypergraph neural networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.750613Z

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Observation a75c72e4-13bd-44c5-b327-90ce857ac1fd · outbound

This paper cites an unresolved cited work.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-05-15T15:51:14.584188Z

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Observation f67c91c3-4ff7-450c-b182-d7056ba4a505 · outbound

This paper cites Size-independent sample complexity of neural networks.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Size-independent sample complexity of neural networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.687457Z

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Observation 270ec3aa-69ea-4333-964b-4cc5d484b1bd · outbound

This paper cites Hypertree decompositions and tractable queries.Journal of Computer and System Sciences, 64(3):579–627.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Hypertree decompositions and tractable queries.Journal of Computer and System Sciences, 64(3):579–627

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.695178Z

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Observation 9c872d7b-a7e2-4790-8a3f-567de3885980 · outbound

This paper cites Topological Deep Learning: Going Beyond Graph Data.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Topological Deep Learning: Going Beyond Graph Data

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:42:58.778491Z

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Observation 40486bad-f023-4799-8112-e15397fed446 · outbound

This paper cites UniGNN: a unified framework for graph and hypergraph neural networks.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks UniGNN: a unified framework for graph and hypergraph neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.719426Z

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Observation d669fb19-4eb5-4a3d-88ea-a772ab9141a6 · outbound

This paper cites Universal invariant and equivariant graph neural networks.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Universal invariant and equivariant graph neural networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.710718Z

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Observation 1a6f14b8-37ca-43ac-8f35-f6a8d7e0cf90 · outbound

This paper cites Equivariant hypergraph neural networks.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Equivariant hypergraph neural networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.634829Z

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Observation 72a4b56f-8d32-4c88-a749-039f825718d0 · outbound

This paper cites Kingma and Jimmy Ba.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Kingma and Jimmy Ba

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.755002Z

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Observation ac6f7fa3-3cad-45ae-81a2-83fe42dd7f28 · outbound

This paper cites Kolda and Brett W.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Kolda and Brett W

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.683464Z

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Observation 676329ac-63af-47a1-8ffb-e6069b643858 · outbound

This paper cites an unresolved cited work.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-05-15T15:51:14.699282Z

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Observation c835bc2b-e3b7-444e-adb5-6028f36af851 · outbound

This paper cites Submodular hypergraphs: p-Laplacians, Cheeger inequalities and spectral clustering.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Submodular hypergraphs: p-Laplacians, Cheeger inequalities and spectral clustering

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.691349Z

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Observation 391187ad-4b52-4ec3-912b-5d856ed5e43a · outbound

This paper cites Implicit Hypergraph Neural Networks: A Stable Framework for Higher-Order Relational Learning with Provable Guarantees.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Implicit Hypergraph Neural Networks: A Stable Framework for Higher-Order Relational Learning with Provable Guarantees

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:42:58.746576Z

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Observation d4e86c7e-350b-4503-bb4e-b3347b890280 · outbound

This paper cites Operations with structures.Acta Mathematica Hungarica, 18(3–4):321–328.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Operations with structures.Acta Mathematica Hungarica, 18(3–4):321–328

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.760299Z

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Observation 280d1923-d3e1-4cc6-8b50-4ac0f13a83c6 · outbound

This paper cites American Mathematical Society.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks American Mathematical Society

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.765208Z

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Observation 339745d8-1f86-4396-a340-c48550d2205f · outbound

This paper cites Limits of dense graph sequences.Journal of Combinatorial Theory, Series B, 96(6):933–957.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Limits of dense graph sequences.Journal of Combinatorial Theory, Series B, 96(6):933–957

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.728223Z

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Observation f82198bd-ec7f-4f6f-b22f-3e19764dd4e5 · outbound

This paper cites Provably powerful graph networks.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Provably powerful graph networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.672579Z

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Observation e6c2a64e-902b-4a1e-861f-b8a7b4c87eb7 · outbound

This paper cites Invariant and equivariant graph networks.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Invariant and equivariant graph networks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.622687Z

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Observation 90d3d28f-ea99-4595-a721-1f85f6475f8f · outbound

This paper cites Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.630588Z

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Observation 50d9a1c0-e907-41ba-bb0d-4e645aa4105b · outbound

This paper cites Kriege, Martin Grohe, Matthias Fey, and Karsten Borgwardt.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Kriege, Martin Grohe, Matthias Fey, and Karsten Borgwardt

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.668509Z

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Observation 99faa40b-0cdf-44f2-b0c2-66492162cba5 · outbound

This paper cites On homomorphism indistinguishability and hypertree depth.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks On homomorphism indistinguishability and hypertree depth

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.784897Z

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Observation 619fc603-1d6d-41be-8512-8bec71a39a02 · outbound

This paper cites Counting homomorphisms from hypergraphs of bounded generalised hypertree width: A logical characterisation.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Counting homomorphisms from hypergraphs of bounded generalised hypertree width: A logical characterisation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.736933Z

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Observation 5497c206-dcbb-4fc4-adb2-5345f6afd866 · outbound

This paper cites A relationship between arbitrary positive matrices and doubly stochastic matrices.Annals of Mathematical Statistics, 35:876–879.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks A relationship between arbitrary positive matrices and doubly stochastic matrices.Annals of Mathematical Statistics, 35:876–879

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.626792Z

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Observation 484b8ce9-91b9-42f9-b335-9f3c03510cc8 · outbound

This paper cites Springer, 2nd edition.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Springer, 2nd edition

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.779776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ce2a1e5d-fc42-4711-80d8-d36accbc595b · outbound

This paper cites Training-free message passing for learning on hypergraphs.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Training-free message passing for learning on hypergraphs

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.810729Z

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Observation 203ff010-5f07-476c-92b1-981dda18fa31 · outbound

This paper cites Equivariant hyper- graph diffusion neural operators.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Equivariant hyper- graph diffusion neural operators

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.610274Z

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Observation 0697474a-7933-4561-8c90-20df47a2bde4 · outbound

This paper cites How powerful are graph neural networks? InInternational Conference on Learning Representations (ICLR).

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks How powerful are graph neural networks? InInternational Conference on Learning Representations (ICLR)

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.732792Z

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Observation b0a25924-8f6a-4d4e-b760-63ba1a08b6bb · outbound

This paper cites HyperGCN: A new method for training graph convolutional networks on hypergraphs.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks HyperGCN: A new method for training graph convolutional networks on hypergraphs

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.740978Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1a639530-0102-410c-94d2-651121d658ac · outbound

This paper cites an unresolved cited work.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-05-15T15:56:14.789356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 54af1f62-a49a-429c-bee7-9971a7f2e195 · outbound

This paper cites Improved expressivity of hypergraph neural networks through high-dimensional generalized Weisfeiler- Leman algorithms.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Improved expressivity of hypergraph neural networks through high-dimensional generalized Weisfeiler- Leman algorithms

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.614582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e1ba8bae-edde-419e-a293-b66f889f553d · outbound

This paper cites Hypergraph limits: A regularity approach.Random Structures & Algorithms, 47 (2):205–226.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Hypergraph limits: A regularity approach.Random Structures & Algorithms, 47 (2):205–226

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.724062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ffa6c35b-eb6f-4f27-9bf0-fb2a964a6414 · outbound

This paper cites ✓” entries satisfy conditions (C1)–(C5). The “△.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks ✓” entries satisfy conditions (C1)–(C5). The “△

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.723708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c391e87f-b8e2-4393-831c-3fe146d1067f · outbound

This paper cites an unresolved cited work.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-05-15T15:56:14.802538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1fb66fc0-05a5-48c4-8580-88312bd2fca5 · outbound

This paper cites Since the Pasch count is an isomorphism invariant, the two systems are provably non-isomorphic.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Since the Pasch count is an isomorphism invariant, the two systems are provably non-isomorphic

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.793954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bad5d740-e054-4f4e-aaf0-7af1d4eed5fa · outbound

This paper cites F.2 The CFI Pair (Native vs.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks F.2 The CFI Pair (Native vs

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.732078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2b3c555e-2cd3-4d3c-a6ef-bb82b466559f · outbound

This paper cites For datasets with large hyperedges (¯k≥20 , e.g., House), we use mean normalization: m(ℓ) e ←m (ℓ) e /|e| and ˜h(ℓ) v ← ˜h(ℓ) v /deg(v).

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks For datasets with large hyperedges (¯k≥20 , e.g., House), we use mean normalization: m(ℓ) e ←m (ℓ) e /|e| and ˜h(ℓ) v ← ˜h(ℓ) v /deg(v)

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.720147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ba0b5b87-a954-4396-994f-728e98ae8087 · outbound

This paper cites ˆt(FP ,star(v))] via Monte Carlo sampling over the star neighborhood star(v) = {e∈ E:v∈e} , using 200 Monte Carlo samples per node (selected from grid {100,200,500} ; Table 10).

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks ˆt(FP ,star(v))] via Monte Carlo sampling over the star neighborhood star(v) = {e∈ E:v∈e} , using 200 Monte Carlo samples per node (selected from grid {100,200,500} ; Table 10)

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.605596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4b668b7e-0202-4fdc-b2be-859e45a12d92 · outbound

This paper cites an unresolved cited work.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-05-15T15:51:14.618298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1d4b5b9e-a7c3-47c6-9c11-ec195b8be5b1 · outbound

This paper cites equation (11)).The gated density embedding is concatenated with the backbone output: ˆyv = MLPout h(L) v ∥g v ·z v ,(12) whereMLP out :R 2dh →R C is a two-layer classifier.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks equation (11)).The gated density embedding is concatenated with the backbone output: ˆyv = MLPout h(L) v ∥g v ·z v ,(12) whereMLP out :R 2dh →R C is a two-layer classifier

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.639038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ef8dd75b-30bf-40a1-899d-f83db4f318bf · outbound

This paper cites During the frozen phase, the model trains as a pure AllDeepSets backbone.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks During the frozen phase, the model trains as a pure AllDeepSets backbone

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.728494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 774ea208-2d02-49cc-91c9-2cc007d6aa9f · outbound

This paper cites Our work synthesizes these into a complete HGNN expressivity characterization.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Our work synthesizes these into a complete HGNN expressivity characterization

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.745899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 946458fb-7bdc-4e39-b56f-64c6078b8aff · outbound

This paper cites edge contribution.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks edge contribution

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.714873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 893b7803-b7ee-456f-961e-1a2789a395b1 · outbound

This paper cites r∗ ≥2 is necessary.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks r∗ ≥2 is necessary

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.675064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7daa5afc-5abb-455a-97aa-dd00ee2a4ebd · outbound

This paper cites Instead, it reduces Senate-Bills accuracy by 5.4 percentage points (90.5%→85.1% ) butimprovesHouse by 4.5 points.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Instead, it reduces Senate-Bills accuracy by 5.4 percentage points (90.5%→85.1% ) butimprovesHouse by 4.5 points

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.588229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 43fb9cb6-bced-4411-90fa-096e9156e534 · outbound

This paper cites A grid search over hidden ∈ {128,256} and J∈ {4,8} yielded at best 80.1%.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks A grid search over hidden ∈ {128,256} and J∈ {4,8} yielded at best 80.1%

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.670406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cdec451b-3113-41d4-9615-d77387609fc7 · outbound

This paper cites Additive fusion constrains density features to the same embedding space as the backbone, limiting the expressivity of the combined representation.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Additive fusion constrains density features to the same embedding space as the backbone, limiting the expressivity of the combined representation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.592975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ffe1d41c-3a14-44e5-8476-d98d8270230e · outbound

This paper cites AllSetTransformer similarly underperformed on the Multi-Order IWS task: 69.7% (vs.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks AllSetTransformer similarly underperformed on the Multi-Order IWS task: 69.7% (vs

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.643334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6c55f66-ef40-45db-b2ec-dfddb8e66cf2 · outbound

This paper cites indifferent.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks indifferent

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:51:14.601178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2f0115b7-bb8f-4252-9ed0-bb59570753ac · outbound

This paper cites On CE-Hard this is expected (any native architecture suffices).

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks On CE-Hard this is expected (any native architecture suffices)

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T15:56:14.688169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 20b64f63-0c91-4ad2-88e2-5912dec88730 · outbound

This paper cites an unresolved cited work.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-05-15T15:56:14.710137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Reference resolution

Outbound references

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