EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks | Paper Citation Record

Observation 968f9238-2559-4bf3-ab73-c6af981e5eee · outbound

This paper cites Molecular representations in ai-driven drug discovery: a review and practical guide.Journal of Cheminformatics, 12(1):56, 2020.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Molecular representations in ai-driven drug discovery: a review and practical guide.Journal of Cheminformatics, 12(1):56, 2020

Reference 1

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Observation 9a43ad3b-4ef5-4def-b915-75130dd54ff3 · outbound

This paper cites A review of molecular representa- tion in the age of machine learning.Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(5):e1603, 2022.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks A review of molecular representa- tion in the age of machine learning.Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(5):e1603, 2022

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:50.092538Z

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Observation 861bca17-0583-4309-bfab-bb05f8651767 · outbound

This paper cites Neural message passing for quantum chemistry.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Neural message passing for quantum chemistry

Reference 3

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Observation 6b19831e-2966-4a75-8061-cd4a793611ab · outbound

This paper cites Modeling polypharmacy side effects with graph convolutional networks.Bioinformatics, 34(13):i457–i466, 2018.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Modeling polypharmacy side effects with graph convolutional networks.Bioinformatics, 34(13):i457–i466, 2018

Reference 4

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Observation 85e78333-c3c1-44af-98a4-2c8cc7c50b6b · outbound

This paper cites Structure-based protein function prediction using graph convolutional networks.Nature communications, 12(1):3168, 2021.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Structure-based protein function prediction using graph convolutional networks.Nature communications, 12(1):3168, 2021

Reference 5

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Observation b119ddf4-ac6b-4a97-a2e6-9acd6ddfa5b8 · outbound

This paper cites Bronstein, Gunnar E.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Bronstein, Gunnar E

Reference 7

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Observation 6ab1385d-49e8-4d14-80ef-c5f6f2342f5b · outbound

This paper cites Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks

Reference 8

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Observation 893ff498-191d-4688-84f3-59f0abd641b6 · outbound

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

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Weisfeiler and lehman go topological: Message passing simplicial networks

Reference 9

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Observation d6a4454e-87f3-498b-a5de-dcecbb0727ee · outbound

This paper cites Weisfeiler and lehman go cellular: Cw networks.Advances in neural information processing systems, 34:2625–2640, 2021.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Weisfeiler and lehman go cellular: Cw networks.Advances in neural information processing systems, 34:2625–2640, 2021

Reference 10

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Observation 776b384a-5a65-43d2-9157-380578a65b01 · outbound

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

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Topological Deep Learning: Going Beyond Graph Data

Reference 11

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Observation d6c08b3c-7217-4bb8-bebe-1012760961c8 · outbound

This paper cites Hnhn: Hypergraph networks with hyperedge neurons.ICML Graph Representation Learning and Beyond Workshop, 2020.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Hnhn: Hypergraph networks with hyperedge neurons.ICML Graph Representation Learning and Beyond Workshop, 2020

Reference 12

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Observation f686c893-0feb-4b8e-8e9a-007293d6ef67 · outbound

This paper cites You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks

Reference 13

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Observation e3014b40-2272-4248-b2db-9b4f5aaf7a4a · outbound

This paper cites Molecular hypergraph neural networks.The Journal of Chemical Physics, 160(14):144307, 2024.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Molecular hypergraph neural networks.The Journal of Chemical Physics, 160(14):144307, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:50.008105Z

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Observation 8440113b-ff8e-4a55-bb76-366f374522c5 · outbound

This paper cites Topological signal processing over simplicial complexes.IEEE Transactions on Signal Processing, 68:2992–3007, 2020.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Topological signal processing over simplicial complexes.IEEE Transactions on Signal Processing, 68:2992–3007, 2020

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.994409Z

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Observation 100389ad-e75b-4ebf-b7af-9cb48b4fefed · outbound

This paper cites Bscnets: Block simplicial complex neural networks.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Bscnets: Block simplicial complex neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.981131Z

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Observation 14e2e61d-07f6-4fe3-80c6-59d0fa109c9d · outbound

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

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 17

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Observation 360280a1-c308-47cc-b4a7-86cb0889be5d · outbound

This paper cites Schnet: A continuous-filter convolutional neural network for modeling quantum interactions.Advances in neural information processing systems, 30, 2017.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Schnet: A continuous-filter convolutional neural network for modeling quantum interactions.Advances in neural information processing systems, 30, 2017

Reference 18

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Observation 023638a6-8907-4b1f-8a27-e50f5fc0e1c9 · outbound

This paper cites Cormorant: Covariant molecular neural networks.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Cormorant: Covariant molecular neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.958741Z

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Observation 88b2387e-394d-4de2-9757-c53f893a65e5 · outbound

This paper cites E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.Nature communications, 13(1):2453, 2022.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.Nature communications, 13(1):2453, 2022

Reference 20

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Observation 6f88a7db-220e-4b4f-962b-ea1a14cf00eb · outbound

This paper cites A universal framework for accurate and efficient geometric deep learning of molecular systems.Scientific Reports, 13(1):19171, 2023.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks A universal framework for accurate and efficient geometric deep learning of molecular systems.Scientific Reports, 13(1):19171, 2023

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.936005Z

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Observation 1e64c4bb-02d0-4d76-a1c7-efc494857314 · outbound

This paper cites Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.Nature Communications, 15(1):313, 2024.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.Nature Communications, 15(1):313, 2024

Reference 22

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Observation 881c9c87-49e8-48f8-94ee-d33d2481e1ba · outbound

This paper cites EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.915027Z

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Observation 39b10359-f067-4f30-924d-fd4cf3c08724 · outbound

This paper cites Protein-nucleic acid complex modeling with frame averaging transformer.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Protein-nucleic acid complex modeling with frame averaging transformer

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.903120Z

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Observation b6d9304a-541f-4958-ba10-84a65b1d5e3f · outbound

This paper cites E (n) equivariant message passing simplicial networks.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks E (n) equivariant message passing simplicial networks

Reference 25

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Observation f11e4c23-dc64-49a7-902b-534b463877eb · outbound

This paper cites E(n) Equivariant Topological Neural Networks.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks E(n) Equivariant Topological Neural Networks

Reference 26

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Observation 84706975-91d8-4ab0-8c9a-aa22242f28b4 · outbound

This paper cites Quantum chemistry structures and properties of 134 kilo molecules.Scientific data, 1(1):1–7, 2014.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Quantum chemistry structures and properties of 134 kilo molecules.Scientific data, 1(1):1–7, 2014

Reference 27

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Observation b9d14959-73c0-4933-9cb7-e0c9e8262c32 · outbound

This paper cites Message-passing neural networks for high- throughput polymer screening.The Journal of chemical physics, 150(23), 2019.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Message-passing neural networks for high- throughput polymer screening.The Journal of chemical physics, 150(23), 2019

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.874967Z

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Observation 469ca954-f018-4c3b-aaaa-cf4fbb0944ad · outbound

This paper cites OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

Reference 29

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Observation 2f9d4e4d-97b3-4f2c-a178-ae2d3744eb64 · outbound

This paper cites Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs

Reference 30

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Observation c6c89ce0-8cf6-4173-821c-a78d95a8eff6 · outbound

This paper cites E (n) equivariant graph neural networks.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks E (n) equivariant graph neural networks

Reference 31

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Observation fb963186-76ff-4b9d-9d08-3a09ebdfd82d · outbound

This paper cites Equiformer: Equivariant graph attention transformer for 3d atomistic graphs.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Equiformer: Equivariant graph attention transformer for 3d atomistic graphs

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.852596Z

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Observation f43f26ba-3ddf-44ca-9422-bdb71135e534 · outbound

This paper cites Graph neural networks: A review of methods and applications.AI open, 1:57–81, 2020.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Graph neural networks: A review of methods and applications.AI open, 1:57–81, 2020

Reference 33

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Observation 3b591702-f213-42ee-aed5-ed477880c667 · outbound

This paper cites Inductive representation learning on large graphs.Advances in neural information processing systems, 30, 2017.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Inductive representation learning on large graphs.Advances in neural information processing systems, 30, 2017

Reference 34

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Observation ee626dc0-735f-42c3-8b0f-f0446c60b904 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Semi-Supervised Classification with Graph Convolutional Networks

Reference 35

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Observation 8ab6e8e6-67a1-46c8-a3be-09b5f1831d79 · outbound

This paper cites How Powerful are Graph Neural Networks?.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks How Powerful are Graph Neural Networks?

Reference 36

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Observation 56494af6-5c16-4083-965f-d11c7893fe6b · outbound

This paper cites Graph attention networks.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Graph attention networks

Reference 37

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Observation bbb06b94-aba1-4c21-b810-b91245014bf4 · outbound

This paper cites Directional message passing for molecular graphs.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Directional message passing for molecular graphs

Reference 38

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This paper cites Gemnet: Universal directional graph neural networks for molecules.Advances in Neural Information Processing Systems, 34:6790–6802, 2021.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Gemnet: Universal directional graph neural networks for molecules.Advances in Neural Information Processing Systems, 34:6790–6802, 2021

Reference 39

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Observation 6c200402-1f0a-4a8f-b8df-c4b0b7834940 · outbound

This paper cites Graph representation learning, deep generative models on graphs, group equivariant molecular neural networks and multiresolution machine learning.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Graph representation learning, deep generative models on graphs, group equivariant molecular neural networks and multiresolution machine learning

Reference 40

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Observation f7cc04c3-5c05-4592-8763-1781380d49f7 · outbound

This paper cites Frame Averaging for Invariant and Equivariant Network Design.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Frame Averaging for Invariant and Equivariant Network Design

Reference 41

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Observation d89355cc-746b-4043-a076-aab329545f43 · outbound

This paper cites Faenet: Frame averaging equivariant gnn for materials modeling.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Faenet: Frame averaging equivariant gnn for materials modeling

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.784152Z

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Observation bb8f55bb-3131-4663-a08a-c75eec6faf7e · outbound

This paper cites Cohen, Mario Geiger, Jonas Köhler, and Max Welling.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Cohen, Mario Geiger, Jonas Köhler, and Max Welling

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.770753Z

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Observation 2a996b41-cf35-43e7-991c-1c74b28175a5 · outbound

This paper cites Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Reference 44

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Observation ad8e8dea-8da2-445f-a8fc-1141323e4239 · outbound

This paper cites Se (3)-transformers: 3d roto-translation equivariant attention networks.Advances in neural information processing systems, 33:1970–1981, 2020.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Se (3)-transformers: 3d roto-translation equivariant attention networks.Advances in neural information processing systems, 33:1970–1981, 2020

Reference 45

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Observation 71d7cfb0-1151-40f0-9f86-9ca22c21f573 · outbound

This paper cites Geometric and physical quantities improve e(3) equivariant message passing.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Geometric and physical quantities improve e(3) equivariant message passing

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.748278Z

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Observation 1a9f51fe-b863-4e64-8658-cb993921afbf · outbound

This paper cites On the expressive power of geometric graph neural networks.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks On the expressive power of geometric graph neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.734337Z

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Observation 94d4dd6c-8ded-4e9f-9c62-1b65898cd7c8 · outbound

This paper cites Anderson, and Risi Kondor.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Anderson, and Risi Kondor

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.722048Z

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Observation d34cbb2c-49aa-4101-8357-85d6def0eb79 · outbound

This paper cites Hypergraph neural networks.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Hypergraph neural networks

Reference 49

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Observation 677041d3-7d27-4fb1-a66a-cb885451e151 · outbound

This paper cites Clifford Group Equivariant Simplicial Message Passing Networks.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Clifford Group Equivariant Simplicial Message Passing Networks

Reference 50

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Observation 4768901a-cef0-46f0-8d93-d2fe26755b05 · outbound

This paper cites Pubchemqc project: a large-scale first-principles electronic structure database for data-driven chemistry.Journal of chemical information and modeling, 57(6):1300–1308, 2017.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Pubchemqc project: a large-scale first-principles electronic structure database for data-driven chemistry.Journal of chemical information and modeling, 57(6):1300–1308, 2017

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.700747Z

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Observation 051f52b8-ab43-4a1a-b763-67b4e3121a26 · outbound

This paper cites Scaling graph neural networks to large proteins.Journal of Chemical Theory and Computation, 2024.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Scaling graph neural networks to large proteins.Journal of Chemical Theory and Computation, 2024

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.687792Z

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Observation 621600d1-61f2-4492-8066-76e890770ba4 · outbound

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EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Unresolved cited work

Reference 53

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Observation a39a5fa8-2266-4a07-b95a-9e779162b3ac · outbound

This paper cites SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:05:49.479322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1925ef2b-b208-4f27-ad24-03fe44acbb18 · outbound

This paper cites Hypergraph geometry reflects higher-order dynamics in protein interaction networks.Scientific Reports, 12(1):20879, 2022.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Hypergraph geometry reflects higher-order dynamics in protein interaction networks.Scientific Reports, 12(1):20879, 2022

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.666146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7618247f-2c9a-4f3b-a653-f88746bb36fa · outbound

This paper cites Predicting ret- rosynthetic pathways using transformer-based models and a hyper-graph exploration strategy.

EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks Predicting ret- rosynthetic pathways using transformer-based models and a hyper-graph exploration strategy

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:49.652424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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