HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models | Paper Citation Record

Observation e5e4613c-3764-433b-bfaf-49758d26b625 · outbound

This paper cites MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing

Reference 1

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Observation 12d742e8-7f0b-4851-b3b8-0e2020fd00be · outbound

This paper cites On the bottleneck of graph neural networks and its practical implications.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models On the bottleneck of graph neural networks and its practical implications

Reference 2

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This paper cites On vanishing gradients, over- smoothing, and over-squashing in gnns: Bridging recurrent and graph learning.Advances in Neural Information Processing Systems, 38:74356–74393, 2026.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models On vanishing gradients, over- smoothing, and over-squashing in gnns: Bridging recurrent and graph learning.Advances in Neural Information Processing Systems, 38:74356–74393, 2026

Reference 3

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Observation 32b0d751-b1ef-495a-8534-06a64517a1b2 · outbound

This paper cites Graph Mamba: Towards Learning on Graphs with State Space Models.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Graph Mamba: Towards Learning on Graphs with State Space Models

Reference 4

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This paper cites Best of both worlds: Advantages of hybrid graph sequence models.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Best of both worlds: Advantages of hybrid graph sequence models

Reference 5

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Observation 686977ea-da7b-4370-99c3-72be226195f3 · outbound

This paper cites Non-backtracking spectrum of random graphs: Community detection and non-regular ramanujan graphs.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Non-backtracking spectrum of random graphs: Community detection and non-regular ramanujan graphs

Reference 6

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Observation 496e49db-2257-464d-8b29-5eb508cdb0a7 · outbound

This paper cites GNN-FiLM: Graph neural networks with feature-wise linear modulation.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models GNN-FiLM: Graph neural networks with feature-wise linear modulation

Reference 7

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Observation 4335ca89-a3b0-4e3c-8c4a-80a0d8d61e12 · outbound

This paper cites Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling

Reference 8

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Observation 61cdba44-b528-4e02-913c-5ef83cd27ba3 · outbound

This paper cites Simple and deep graph convolutional networks.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Simple and deep graph convolutional networks

Reference 9

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Observation 195cf970-cfe5-45c4-8371-2bf83db8d8f4 · outbound

This paper cites Adaptive universal generalized PageRank graph neural network.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Adaptive universal generalized PageRank graph neural network

Reference 10

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Observation a4030367-a2fe-4105-8948-f53b1f382c02 · outbound

This paper cites Chung.Spectral Graph Theory.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Chung.Spectral Graph Theory

Reference 11

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Observation 4f4165fd-f86a-4d64-97f2-88f570f2e519 · outbound

This paper cites Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality

Reference 12

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Observation a159441d-fd1b-4871-ad23-54f74b4d8cb8 · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Convolutional neural networks on graphs with fast localized spectral filtering

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:47.204973Z

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Observation 2f6b04f1-c4c9-46ce-9fab-e8bb2f904241 · outbound

This paper cites Konstantin Rusch, Michael M.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Konstantin Rusch, Michael M

Reference 14

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Observation 246dad70-d6e2-4a10-9032-1b8080b567e2 · outbound

This paper cites Long range graph benchmark.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Long range graph benchmark

Reference 15

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Observation 3dfbbbf5-8e7e-464a-ade6-e034112ab562 · outbound

This paper cites SIGN: Scalable Inception Graph Neural Networks.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models SIGN: Scalable Inception Graph Neural Networks

Reference 16

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Observation c58a9052-cdca-4038-889c-5dcc95a5a28a · outbound

This paper cites Predict then propagate: Graph neural networks meet personalized PageRank.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Predict then propagate: Graph neural networks meet personalized PageRank

Reference 17

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Observation e480bea8-87d4-42aa-8c47-89772386e18f · outbound

This paper cites Schoenholz, Patrick F.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Schoenholz, Patrick F

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:46.675388Z

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Observation 8c6f2fcb-5ed5-4f04-941c-ba8e7a5b56e5 · outbound

This paper cites Mamba: Linear-time sequence modeling with selective state spaces.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Mamba: Linear-time sequence modeling with selective state spaces

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:46.604866Z

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Observation 1ebb6082-6b6a-4c8d-8db2-a8833a376bcc · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Efficiently Modeling Long Sequences with Structured State Spaces

Reference 20

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Observation eab1f8ad-ff3c-4b64-a5a9-cf3b92d477cc · outbound

This paper cites Bronstein, and Francesco Di Giovanni.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Bronstein, and Francesco Di Giovanni

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:46.538156Z

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Observation 20c604f8-7d0e-4b21-b93f-0b51d0b7541c · outbound

This paper cites Dai, and Quoc V.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Dai, and Quoc V

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:46.436470Z

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Observation 53a55628-748c-483e-8951-7e37b6e8f4ec · outbound

This paper cites Hamilton, Rex Ying, and Jure Leskovec.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Hamilton, Rex Ying, and Jure Leskovec

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:46.380595Z

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Observation 6a15d453-344d-488b-9c45-7eca142b4c92 · outbound

This paper cites Zeta functions of finite graphs and representations of p-adic groups.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Zeta functions of finite graphs and representations of p-adic groups

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:46.290732Z

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Observation a5d3366d-41c8-4e45-84d4-78f3fd4b9a6a · outbound

This paper cites BernNet: Learning arbitrary graph spectral filters via bernstein approximation.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models BernNet: Learning arbitrary graph spectral filters via bernstein approximation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:46.216956Z

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Observation 61e09ef5-7158-45fd-b487-58bd3a2c3f8b · outbound

This paper cites What Can We Learn from State Space Models for Machine Learning on Graphs?.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models What Can We Learn from State Space Models for Machine Learning on Graphs?

Reference 26

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Observation aa0d0ea1-a203-4b8c-b136-6b39591e9b6c · outbound

This paper cites Banerjee, and Guido Montúfar.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Banerjee, and Guido Montúfar

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:46.158235Z

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Observation bf43c3da-9435-461a-9269-ca7842d15a35 · outbound

This paper cites Revisiting Random Walks for Learning on Graphs.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Revisiting Random Walks for Learning on Graphs

Reference 28

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Observation ff3b7279-e67e-4807-acbc-7aecfcd1ef82 · outbound

This paper cites Kipf and Max Welling.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Kipf and Max Welling

Reference 29

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Observation 200ac0de-4b80-4780-b1af-3e364065eb0d · outbound

This paper cites Diffusion im- proves graph learning.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Diffusion im- proves graph learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:46.034773Z

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Observation 58c6dfc1-c92c-4952-a4da-fe38e5ad97e9 · outbound

This paper cites Hamilton, Vincent Létourneau, and Prudencio Tossou.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Hamilton, Vincent Létourneau, and Prudencio Tossou

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:45.932074Z

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Observation a37dd2e5-5680-463e-a779-66a21e18092f · outbound

This paper cites Spectral redemption in clustering sparse networks.Proceedings of the National Academy of Sciences, 110(52):20935–20940, 2013.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Spectral redemption in clustering sparse networks.Proceedings of the National Academy of Sciences, 110(52):20935–20940, 2013

Reference 32

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Observation 35eea304-dc35-4819-8e1e-83106a85ecec · outbound

This paper cites Kosiorek, Seungjin Choi, and Yee Whye Teh.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Kosiorek, Seungjin Choi, and Yee Whye Teh

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:45.851176Z

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Observation 5e4007ec-5628-418f-a170-5c97432e855d · outbound

This paper cites Deeper insights into graph convolutional networks for semi-supervised learning.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Deeper insights into graph convolutional networks for semi-supervised learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:45.779411Z

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Observation e9eb827b-dbef-4fb0-8a16-b901186650d6 · outbound

This paper cites Towards deeper graph neural networks.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Towards deeper graph neural networks

Reference 35

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Observation bf4b673b-250a-4cc3-857e-4e435ae70367 · outbound

This paper cites LRIM: a physics-based benchmark for provably evaluating long-range capabilities in graph learning.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models LRIM: a physics-based benchmark for provably evaluating long-range capabilities in graph learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:45.737725Z

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Observation 4fed509e-f62d-495c-a5fa-23c79ac0b869 · outbound

This paper cites From Message-Passing to Linearized Graph Sequence Models.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models From Message-Passing to Linearized Graph Sequence Models

Reference 37

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Observation 1f0c5b6d-6897-489b-a459-b19d8bf4d4f9 · outbound

This paper cites Can you hear me now? a benchmark for long-range graph propagation.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Can you hear me now? a benchmark for long-range graph propagation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:45.691410Z

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Observation a66c108d-7088-4db3-89b6-3cc219b8083c · outbound

This paper cites Graph neural networks exponentially lose expressive power for node classification.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Graph neural networks exponentially lose expressive power for node classification

Reference 39

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Observation ee55a82b-5d7f-4030-a32a-d454e50f20a3 · outbound

This paper cites Smith, Albert Gu, Anushan Fernando, Ça˘glar Gülçehre, Razvan Pascanu, and Soham De.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Smith, Albert Gu, Anushan Fernando, Ça˘glar Gülçehre, Razvan Pascanu, and Soham De

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:45.621550Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 87c2ad3b-16c3-4d8e-8ffe-34a5fff5996e · outbound

This paper cites Recipe for a general, powerful, scalable graph trans- former.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Recipe for a general, powerful, scalable graph trans- former

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:45.568308Z

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Observation d6051e9b-0b0f-4672-8b21-54b48fd62ff4 · outbound

This paper cites DropEdge: Towards deep graph convolutional networks on node classification.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models DropEdge: Towards deep graph convolutional networks on node classification

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:45.465690Z

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Observation abc547b7-1c55-4891-83da-dc44ab0ee184 · outbound

This paper cites A Survey on Oversmoothing in Graph Neural Networks.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models A Survey on Oversmoothing in Graph Neural Networks

Reference 43

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Observation 2f0a197e-50a1-4d6a-8870-9541b84c4248 · outbound

This paper cites Sutherland, and Ali Kemal Sinop.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Sutherland, and Ali Kemal Sinop

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:45.406074Z

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Observation 8c1c9254-c640-49b1-b041-5fc9554f5628 · outbound

This paper cites Walking out of the weisfeiler leman hierarchy: Graph learning beyond message passing.Transactions on Machine Learning Research, 2023.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Walking out of the weisfeiler leman hierarchy: Graph learning beyond message passing.Transactions on Machine Learning Research, 2023

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:45.360936Z

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Observation 5b318b0b-a49e-4525-b602-576d3e86748b · outbound

This paper cites Bronstein.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Bronstein

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:45.284531Z

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Observation 94592e62-177f-41d0-92bd-df767cd90834 · outbound

This paper cites Gomez, Łukasz Kaiser, and Illia Polosukhin.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:45.222044Z

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Observation 5d89bbfd-ecf9-4388-a9bf-7d53d7acdcb1 · outbound

This paper cites Graph attention networks.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Graph attention networks

Reference 48

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Observation 3391a199-f846-41f5-8e95-42c4628dc3cc · outbound

This paper cites Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 49

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Observation ff848bfe-d93f-40c5-b01d-ea00fb651e2e · outbound

This paper cites Representation learning on graphs with jumping knowledge networks.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Representation learning on graphs with jumping knowledge networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:44.975322Z

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Observation 461edc02-b373-45f8-ab5e-e24be5e80108 · outbound

This paper cites How powerful are graph neural networks? InInternational Conference on Learning Representations, 2019.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models How powerful are graph neural networks? InInternational Conference on Learning Representations, 2019

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:44.880500Z

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Observation 2911e4f3-3bbc-4a1d-bab0-0b8f442c89a9 · outbound

This paper cites Do transformers really perform badly for graph repre- sentation? InAdvances in Neural Information Processing Systems, volume 34, pp.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Do transformers really perform badly for graph repre- sentation? InAdvances in Neural Information Processing Systems, volume 34, pp

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:44.726883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 761ca390-9118-4010-a2fa-e4f14b2b9ad4 · outbound

This paper cites an unresolved cited work.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-14T04:24:44.605931Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 841424d4-7f64-48e0-9447-6a3b46d468b0 · outbound

This paper cites Adaptive diffusion in graph neural networks.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Adaptive diffusion in graph neural networks

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:44.166919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c1eb8a51-8302-4645-a75c-d7179d665de7 · outbound

This paper cites PairNorm: Tackling oversmoothing in GNNs.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models PairNorm: Tackling oversmoothing in GNNs

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:44.138530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d1c35821-a6b5-465b-8a40-bfc0e88e481c · outbound

This paper cites In particular,∥∂[T r(S)H]u/∂Hv∥2 = 2r−1|[Sr]uv|, whereas standard power propagation satisfies ∥∂[SrH]u/∂Hv∥2 =|[S r]uv|.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models In particular,∥∂[T r(S)H]u/∂Hv∥2 = 2r−1|[Sr]uv|, whereas standard power propagation satisfies ∥∂[SrH]u/∂Hv∥2 =|[S r]uv|

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:44.112349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a4f1bdaa-fa79-47fc-8828-6c8ed0fb31db · outbound

This paper cites This proves the result.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models This proves the result

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:24:44.057436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 45c34b46-9b7c-4437-ae19-5be9cb4a00f8 · outbound

This paper cites an unresolved cited work.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Unresolved cited work

Reference 2018

Resolution: unresolved; raw_fallback, observed 2026-08-14T04:24:45.074823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9fef965b-0bb0-4832-a31b-92bca6998348 · outbound

This paper cites an unresolved cited work.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Unresolved cited work

Reference 2020

Resolution: unresolved; raw_fallback, observed 2026-08-14T04:24:47.665746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f4e13a0f-3cb7-4079-b792-ad42853ad706 · outbound

This paper cites an unresolved cited work.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Unresolved cited work

Reference 2021

Resolution: unresolved; raw_fallback, observed 2026-08-14T04:24:47.435079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ac4e6f8d-bb46-4996-9c4c-bdb4a9fd2692 · outbound

This paper cites an unresolved cited work.

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-14T04:24:46.958955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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