Paper Citation Record · LEDGER

Improving the Effective Receptive Field of Message-Passing Neural Networks

As of 17 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 1 inbound Pith citation observation for arXiv:2505.23185.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.23185 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:57:36.791084Z

measured 71 of 71 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T17:21:28.096446Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T00:17:29.163472Z

Reference resolution

70 of 70 outbound references displayed

verified exact3
verified fuzzy49
unresolved18
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation afde84fd-a7c6-42c5-bf59-e67a80d82190 · outbound

This paper cites write newline.

Improving the Effective Receptive Field of Message-Passing Neural Networks write newline

Reference 1

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Source-reported events for the cited work

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Observation e33a9266-85da-4ab0-bfdd-04a7bd599568 · outbound

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

Improving the Effective Receptive Field of Message-Passing Neural Networks Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:45.257273Z

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Observation 81e36e1d-fcaf-42e3-883d-520500613816 · outbound

This paper cites Slic superpixels, 2010.

Improving the Effective Receptive Field of Message-Passing Neural Networks Slic superpixels, 2010

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:45.043177Z

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Observation b4e76f8b-e4fb-439c-b126-9ef7fb2c9cae · outbound

This paper cites and Yahav, E.

Improving the Effective Receptive Field of Message-Passing Neural Networks and Yahav, E

Reference 4

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Observation e8d69591-2253-4248-a0e6-b27710490738 · outbound

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

Improving the Effective Receptive Field of Message-Passing Neural Networks Graph Mamba: Towards Learning on Graphs with State Space Models

Reference 5

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Observation bb8179ac-ff07-4f80-83a1-20b6e978ba4d · outbound

This paper cites and Niyogi, P.

Improving the Effective Receptive Field of Message-Passing Neural Networks and Niyogi, P

Reference 6

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Observation fe4f06b6-f5be-422b-8631-49dfb7f884ac · outbound

This paper cites Beyond low-frequency information in graph convolutional networks.

Improving the Effective Receptive Field of Message-Passing Neural Networks Beyond low-frequency information in graph convolutional networks

Reference 7

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Observation 0cb38f1a-626a-4f10-b0a5-b71934ebca52 · outbound

This paper cites Residual Gated Graph ConvNets.

Improving the Effective Receptive Field of Message-Passing Neural Networks Residual Gated Graph ConvNets

Reference 8

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Observation 60c549ed-c723-4568-a36c-c44e04f9edc4 · outbound

This paper cites B., Lodi, A., Morris, C., and Veli c kovi \'c , P.

Improving the Effective Receptive Field of Message-Passing Neural Networks B., Lodi, A., Morris, C., and Veli c kovi \'c , P

Reference 9

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Observation 4bbd68d3-61c3-4876-9c82-7059f7ed5a55 · outbound

This paper cites Beltrami flow and neural diffusion on graphs.

Improving the Effective Receptive Field of Message-Passing Neural Networks Beltrami flow and neural diffusion on graphs

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:44.365548Z

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Observation 08fb2c8c-2c7f-4304-b804-d8ca86f953a6 · outbound

This paper cites P., Rowbottom, J., Gorinova, M., Webb, S., Rossi, E., and Bronstein, M.

Improving the Effective Receptive Field of Message-Passing Neural Networks P., Rowbottom, J., Gorinova, M., Webb, S., Rossi, E., and Bronstein, M

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:44.181798Z

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Observation 26ff53bb-ec64-4b99-bb8a-c6d435b35188 · outbound

This paper cites Improving message-passing gnns by asynchronous aggregation.

Improving the Effective Receptive Field of Message-Passing Neural Networks Improving message-passing gnns by asynchronous aggregation

Reference 12

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Observation d67b4b0a-9338-48da-9bee-15810cd05bdf · outbound

This paper cites Adaptive universal generalized pagerank graph neural network.

Improving the Effective Receptive Field of Message-Passing Neural Networks Adaptive universal generalized pagerank graph neural network

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:43.830481Z

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Observation 5412cd9a-732d-4b7b-9ab8-3ac66063438c · outbound

This paper cites Gread: Graph neural reaction-diffusion networks.

Improving the Effective Receptive Field of Message-Passing Neural Networks Gread: Graph neural reaction-diffusion networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:43.664669Z

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Observation a3ae2331-a707-437e-9311-329dc08a11fe · outbound

This paper cites S., Guan, Y., and Kulis, B.

Improving the Effective Receptive Field of Message-Passing Neural Networks S., Guan, Y., and Kulis, B

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:43.533397Z

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Observation 79a1eb58-0640-450c-b909-257c6203f48d · outbound

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Improving the Effective Receptive Field of Message-Passing Neural Networks Unresolved cited work

Reference 16

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Observation 11c49cb2-fef4-4c3c-9c77-146f104625a5 · outbound

This paper cites Scaling up your kernels to 31x31: Revisiting large kernel design in cnns.

Improving the Effective Receptive Field of Message-Passing Neural Networks Scaling up your kernels to 31x31: Revisiting large kernel design in cnns

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:43.277371Z

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Observation 0686b954-8889-45ca-836c-ee7dbcb7f48e · outbound

This paper cites Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily.

Improving the Effective Receptive Field of Message-Passing Neural Networks Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:43.112506Z

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Observation 169a15e7-58c4-45cf-984d-4a3058e2041b · outbound

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Improving the Effective Receptive Field of Message-Passing Neural Networks Unresolved cited work

Reference 19

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Observation ab2cecc9-3fc9-464e-a9f0-a8b39e938c2c · outbound

This paper cites P., Ramp \'a s ek, L., Galkin, M., Parviz, A., Wolf, G., Luu, A.

Improving the Effective Receptive Field of Message-Passing Neural Networks P., Ramp \'a s ek, L., Galkin, M., Parviz, A., Wolf, G., Luu, A

Reference 20

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Observation 7ae69f08-b3b3-471a-837a-dd953a71031a · outbound

This paper cites J., and Treister, E.

Improving the Effective Receptive Field of Message-Passing Neural Networks J., and Treister, E

Reference 21

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Observation f571aa95-027c-4896-a804-50859992daec · outbound

This paper cites Improving graph neural networks with learnable propagation operators.

Improving the Effective Receptive Field of Message-Passing Neural Networks Improving graph neural networks with learnable propagation operators

Reference 22

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Observation 1f7ebd7c-bb97-49f5-92c7-82f65ba68e04 · outbound

This paper cites K., Winn, J., and Zisserman, A.

Improving the Effective Receptive Field of Message-Passing Neural Networks K., Winn, J., and Zisserman, A

Reference 23

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This paper cites Graph neural networks for social recommendation.

Improving the Effective Receptive Field of Message-Passing Neural Networks Graph neural networks for social recommendation

Reference 24

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Observation 72aaddcd-984a-4c83-a18b-0304ef2b173e · outbound

This paper cites E., Amoyal, R., Treister, E., and Freifeld, O.

Improving the Effective Receptive Field of Message-Passing Neural Networks E., Amoyal, R., Treister, E., and Freifeld, O

Reference 25

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This paper cites M., and Ceylan, I.

Improving the Effective Receptive Field of Message-Passing Neural Networks M., and Ceylan, I

Reference 26

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Observation 2ee19311-51ee-4518-bd0c-ba9239c0f2f8 · outbound

This paper cites and Ji, S.

Improving the Effective Receptive Field of Message-Passing Neural Networks and Ji, S

Reference 27

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Observation 3d4e98b8-8793-42d1-b59a-5898e58d5fad · outbound

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Improving the Effective Receptive Field of Message-Passing Neural Networks Diffusion improves graph learning

Reference 28

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Observation 3e80009f-dc6a-4ee4-93e1-9be07c5a40fb · outbound

This paper cites On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems.

Improving the Effective Receptive Field of Message-Passing Neural Networks On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems

Reference 29

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Observation 14dceaea-ab4e-49eb-8d44-548267920ac3 · outbound

This paper cites M., and Di Giovanni, F.

Improving the Effective Receptive Field of Message-Passing Neural Networks M., and Di Giovanni, F

Reference 30

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Observation 7f8b77d2-5a43-48be-b500-ab750c4420ed · outbound

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Improving the Effective Receptive Field of Message-Passing Neural Networks Inductive representation learning on large graphs

Reference 31

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This paper cites From continuous dynamics to graph neural networks: Neural diffusion and beyond.

Improving the Effective Receptive Field of Message-Passing Neural Networks From continuous dynamics to graph neural networks: Neural diffusion and beyond

Reference 32

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Observation 1969b364-bc3b-45a8-a13f-02da3e1f0b92 · outbound

This paper cites Strategies for pre-training graph neural networks.

Improving the Effective Receptive Field of Message-Passing Neural Networks Strategies for pre-training graph neural networks

Reference 33

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Observation f83e3a2f-1ded-470a-b642-2c44941acf68 · outbound

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Improving the Effective Receptive Field of Message-Passing Neural Networks Semi-Supervised Classification with Graph Convolutional Networks

Reference 34

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Improving the Effective Receptive Field of Message-Passing Neural Networks B., and Goldstein, T

Reference 35

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Improving the Effective Receptive Field of Message-Passing Neural Networks Rethinking graph transformers with spectral attention

Reference 36

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This paper cites Finding global homophily in graph neural networks when meeting heterophily.

Improving the Effective Receptive Field of Message-Passing Neural Networks Finding global homophily in graph neural networks when meeting heterophily

Reference 37

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Improving the Effective Receptive Field of Message-Passing Neural Networks Unresolved cited work

Reference 38

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This paper cites Toloker graph: Interaction of crowd annotators, 2023.

Improving the Effective Receptive Field of Message-Passing Neural Networks Toloker graph: Interaction of crowd annotators, 2023

Reference 39

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Observation 34848461-cf78-4b71-9bf6-2ced2d024e3c · outbound

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Improving the Effective Receptive Field of Message-Passing Neural Networks Unresolved cited work

Reference 40

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Improving the Effective Receptive Field of Message-Passing Neural Networks Geniepath: Graph neural networks with adaptive receptive paths

Reference 41

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Observation 16a14594-415a-4b90-9c82-f70afd140c40 · outbound

This paper cites Understanding the effective receptive field in deep convolutional neural networks.

Improving the Effective Receptive Field of Message-Passing Neural Networks Understanding the effective receptive field in deep convolutional neural networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:40.474560Z

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This paper cites Transformers for capturing multi-level graph structure using hierarchical distances.

Improving the Effective Receptive Field of Message-Passing Neural Networks Transformers for capturing multi-level graph structure using hierarchical distances

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:40.336734Z

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This paper cites Improving graph neural networks with structural adaptive receptive fields.

Improving the Effective Receptive Field of Message-Passing Neural Networks Improving graph neural networks with structural adaptive receptive fields

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:40.208070Z

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Observation 328c6552-41bb-4a46-8b48-a3a26a48a7de · outbound

This paper cites Learning discrete adaptive receptive fields for graph convolutional networks.

Improving the Effective Receptive Field of Message-Passing Neural Networks Learning discrete adaptive receptive fields for graph convolutional networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:40.073549Z

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Observation 588401e7-10c6-4007-8945-048aa6db154e · outbound

This paper cites QDC : Quantum diffusion convolution kernels on graphs.

Improving the Effective Receptive Field of Message-Passing Neural Networks QDC : Quantum diffusion convolution kernels on graphs

Reference 46

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Improving the Effective Receptive Field of Message-Passing Neural Networks A fractional graph laplacian approach to oversmoothing

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:39.716509Z

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This paper cites K., Liu, X., and Murata, T.

Improving the Effective Receptive Field of Message-Passing Neural Networks K., Liu, X., and Murata, T

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:39.514647Z

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This paper cites Attending to graph transformers.

Improving the Effective Receptive Field of Message-Passing Neural Networks Attending to graph transformers

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:39.246222Z

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Observation d192cf1a-73f6-4830-9d63-48ffb7e0e429 · outbound

This paper cites and Duta, I.

Improving the Effective Receptive Field of Message-Passing Neural Networks and Duta, I

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:39.053809Z

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Observation 33745588-61d9-4a87-8730-8506e14cf4f9 · outbound

This paper cites Revisiting Graph Neural Networks: All We Have is Low-Pass Filters.

Improving the Effective Receptive Field of Message-Passing Neural Networks Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

Reference 51

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Improving the Effective Receptive Field of Message-Passing Neural Networks Unresolved cited work

Reference 52

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Observation ba976018-b4fe-425a-bf0f-f4d2c62f740e · outbound

This paper cites A critical look at the evaluation of GNN s under heterophily: Are we really making progress? In The Eleventh International Conference on Learning Representations, 2023.

Improving the Effective Receptive Field of Message-Passing Neural Networks A critical look at the evaluation of GNN s under heterophily: Are we really making progress? In The Eleventh International Conference on Learning Representations, 2023

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:38.856186Z

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Observation e0f51fd9-2884-4845-b8fe-3aa899a5caad · outbound

This paper cites P., Luu, A.

Improving the Effective Receptive Field of Message-Passing Neural Networks P., Luu, A

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:38.735157Z

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Observation ca86a12b-1d82-4559-aa12-9111e2eacb2d · outbound

This paper cites Masked label prediction: Unified message passing model for semi-supervised classification.

Improving the Effective Receptive Field of Message-Passing Neural Networks Masked label prediction: Unified message passing model for semi-supervised classification

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:38.522055Z

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Observation fdbbd629-0ce6-41b3-9c44-1d4e975b5f35 · outbound

This paper cites J., and Sinop, A.

Improving the Effective Receptive Field of Message-Passing Neural Networks J., and Sinop, A

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:38.430310Z

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Observation c6e93d45-e3fa-4d97-a667-6b6a16ffde98 · outbound

This paper cites Graph neural networks in particle physics.

Improving the Effective Receptive Field of Message-Passing Neural Networks Graph neural networks in particle physics

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:38.249682Z

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Observation ae709be8-aec8-45ac-b041-4961a40296c6 · outbound

This paper cites Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark.

Improving the Effective Receptive Field of Message-Passing Neural Networks Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:57:36.881621Z

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Observation d7acf053-e1bf-4f54-89d2-5e4395ccd2fc · outbound

This paper cites P., Dong, X., and Bronstein, M.

Improving the Effective Receptive Field of Message-Passing Neural Networks P., Dong, X., and Bronstein, M

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:38.026708Z

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Observation c9b942a8-cc16-4fdd-8647-52d8dbbf878b · outbound

This paper cites Capturing graphs with hypo-elliptic diffusions.

Improving the Effective Receptive Field of Message-Passing Neural Networks Capturing graphs with hypo-elliptic diffusions

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:37.912471Z

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Observation c75b3696-be8e-4747-81ce-220b0ea9003e · outbound

This paper cites Graph attention networks.

Improving the Effective Receptive Field of Message-Passing Neural Networks Graph attention networks

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:37.759964Z

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Observation 9444bcba-b433-4059-97af-10c039fde993 · outbound

This paper cites Next Level Message-Passing with Hierarchical Support Graphs.

Improving the Effective Receptive Field of Message-Passing Neural Networks Next Level Message-Passing with Hierarchical Support Graphs

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T12:57:36.260923Z

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Observation d5f4e567-3810-4e66-adad-586a100e8ed9 · outbound

This paper cites and Zhang, M.

Improving the Effective Receptive Field of Message-Passing Neural Networks and Zhang, M

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:37.670245Z

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Observation 3cda1c15-5362-4b0a-9f96-e265111c58a7 · outbound

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

Improving the Effective Receptive Field of Message-Passing Neural Networks How powerful are graph neural networks? In International Conference on Learning Representations, 2019

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T12:57:36.447212Z

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Observation 81246b9e-1c31-4fb9-a290-38b7b14456e9 · outbound

This paper cites Sebot: Structural entropy guided multi-view contrastive learning for social bot detection.

Improving the Effective Receptive Field of Message-Passing Neural Networks Sebot: Structural entropy guided multi-view contrastive learning for social bot detection

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:37.551460Z

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Observation 3d0e725d-9e2a-4b2e-9bff-45afd0bb3ecf · outbound

This paper cites Hierarchical graph representation learning with differentiable pooling.

Improving the Effective Receptive Field of Message-Passing Neural Networks Hierarchical graph representation learning with differentiable pooling

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T12:57:36.617755Z

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Observation c0ad34d5-05eb-4dbe-ad5b-5dfa4f48a978 · outbound

This paper cites Hierarchical graph transformer with adaptive node sampling.

Improving the Effective Receptive Field of Message-Passing Neural Networks Hierarchical graph transformer with adaptive node sampling

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:37.364678Z

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Observation 3d58e2d8-09cd-40c5-ba30-7a27e179f876 · outbound

This paper cites Hierarchical message-passing graph neural networks.

Improving the Effective Receptive Field of Message-Passing Neural Networks Hierarchical message-passing graph neural networks

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T12:57:36.712950Z

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Observation b9096daa-95ac-4966-a829-ca478f9cf2f6 · outbound

This paper cites Beyond homophily in graph neural networks: Current limitations and effective designs.

Improving the Effective Receptive Field of Message-Passing Neural Networks Beyond homophily in graph neural networks: Current limitations and effective designs

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:37.226313Z

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Observation 38e56155-aaae-4357-bf36-2a042a39af06 · outbound

This paper cites A., Rao, A., Mai, T., Lipka, N., Ahmed, N.

Improving the Effective Receptive Field of Message-Passing Neural Networks A., Rao, A., Mai, T., Lipka, N., Ahmed, N

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T12:57:36.791084Z

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Pith citing papers

Observation 72de4010-f32a-46f7-97d9-180039ebeaff · inbound

Beyond Convolution: Advancing Hypergraph Neural Networks with Hypergraph U-Nets cites this paper.

Beyond Convolution: Advancing Hypergraph Neural Networks with Hypergraph U-Nets Improving the Effective Receptive Field of Message-Passing Neural Networks

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:17:29.165125Z

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