Paper Citation Record · LEDGER

CoRe-GNN: Multilevel Message passing on Coarsened graphs

As of 7 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2608.02128.

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

pith.paper-citation-record.v1
2608.02128 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:42:49.589630Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy0
unresolved39
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 27f6314e-99e6-430b-a792-d2eb3098cf70 · outbound

This paper cites Spectral clustering with graph neural networks for graph pooling.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Spectral clustering with graph neural networks for graph pooling

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.313165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e02b65e9-f064-4908-bcb4-9fd6466b5ce2 · outbound

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

CoRe-GNN: Multilevel Message passing on Coarsened graphs Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.377357Z

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Observation bf93c4a4-b620-4435-8ea1-8ce5d4d56d89 · outbound

This paper cites FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling.

CoRe-GNN: Multilevel Message passing on Coarsened graphs FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.472269Z

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Observation 1d0370ed-e74a-4fba-a9a2-d33c1a61b0a7 · outbound

This paper cites Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.558906Z

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Observation 4290b8e5-3a5b-4138-a141-3ba52c3f79f8 · outbound

This paper cites Spectral graph coarsening using inner product preservation and the grassmann manifold.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Spectral graph coarsening using inner product preservation and the grassmann manifold

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.666071Z

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Observation e1fb026f-72e3-4f52-b27f-3980254176a1 · outbound

This paper cites Weighted graph cuts without eigenvectors a multilevel approach.IEEE transactions on pattern analysis and machine intelligence, 29(11):1944–1957, 2007.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Weighted graph cuts without eigenvectors a multilevel approach.IEEE transactions on pattern analysis and machine intelligence, 29(11):1944–1957, 2007

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.748306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a72fac24-b66c-40ab-8999-4e1a649ca2dd · outbound

This paper cites Graph coarsening via convolution matching for scalable graph neural network training.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Graph coarsening via convolution matching for scalable graph neural network training

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.809625Z

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Unavailable: canonical work link unavailable.

Observation 5f96d411-724c-4495-a6a0-5e39915124ca · outbound

This paper cites Vq-gnn: A universal framework to scale up graph neural networks using vector quantization.Advances in Neural Information Processing Systems, 34:6733–6746, 2021.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Vq-gnn: A universal framework to scale up graph neural networks using vector quantization.Advances in Neural Information Processing Systems, 34:6733–6746, 2021

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.883869Z

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Observation def06234-5f9f-46da-a604-9ec4fb2ec4d1 · outbound

This paper cites Learning on large graphs using intersecting communities.Advances in Neural Information Processing Systems, 37:57349–57388, 2024.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Learning on large graphs using intersecting communities.Advances in Neural Information Processing Systems, 37:57349–57388, 2024

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:45.947872Z

Source-reported events for the cited work

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Observation 91f2b638-1014-40f0-961e-a3ed90664a2f · outbound

This paper cites Graph u-nets.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Graph u-nets

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.009919Z

Source-reported events for the cited work

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Observation 37fcb282-e570-4ae2-b60b-345e7c5fe4b6 · outbound

This paper cites Influence-based mini-batching for graph neural networks.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Influence-based mini-batching for graph neural networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.126062Z

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Observation 2600d8ca-f106-4eee-9224-3c011d7412a7 · outbound

This paper cites Neural Message Passing for Quantum Chemistry.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Neural Message Passing for Quantum Chemistry

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.248029Z

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Observation 926e7392-dc73-4b14-83ca-99a7316a7b2c · outbound

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

CoRe-GNN: Multilevel Message passing on Coarsened graphs Inductive representation learning on large graphs.Advances in neural information processing systems, 30, 2017

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.356629Z

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Observation 156aeeef-7232-4414-bdb5-11796a14ebf0 · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs.Advances in neural information processing systems, 33: 22118–22133, 2020.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Open graph benchmark: Datasets for machine learning on graphs.Advances in neural information processing systems, 33: 22118–22133, 2020

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.436861Z

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Unavailable: canonical work link unavailable.

Observation b8c9aa2a-7c56-456a-89aa-6104dd95722f · outbound

This paper cites Condensing graphs via one-step gradient matching.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Condensing graphs via one-step gradient matching

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.514140Z

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Unavailable: canonical work link unavailable.

Observation d3fedeb7-e092-45bb-afdc-abd60fff8340 · outbound

This paper cites Graph Coarsening with Message-Passing Guarantees.Advances in Neural Information Processing Systems, 37:114902–114927, 2024.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Graph Coarsening with Message-Passing Guarantees.Advances in Neural Information Processing Systems, 37:114902–114927, 2024

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.557103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad7c9793-9364-416f-a899-3f92476e0dfe · outbound

This paper cites Taxonomy of reduction matrices for graph coarsening.Advances in Neural Information Processing Systems, 38:96058–96083, 2026.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Taxonomy of reduction matrices for graph coarsening.Advances in Neural Information Processing Systems, 38:96058–96083, 2026

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.620554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbe82ac5-a30f-4ec4-b754-6335fb3893d0 · outbound

This paper cites Metis: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Metis: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.647734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fe69eb9-3a30-4ccd-bc18-78ee96c0f3f4 · outbound

This paper cites Ugc: Universal graph coarsening.Advances in Neural Information Processing Systems, 37:63057–63081, 2024.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Ugc: Universal graph coarsening.Advances in Neural Information Processing Systems, 37:63057–63081, 2024

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.734614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a6fc6c2-4ec9-46f6-b67d-ad126442042d · outbound

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

CoRe-GNN: Multilevel Message passing on Coarsened graphs Semi-Supervised Classification with Graph Convolutional Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.894279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02e14812-a6e9-489f-840a-03f26b3638c6 · outbound

This paper cites Efficient learning on large graphs using a densifying regularity lemma.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Efficient learning on large graphs using a densifying regularity lemma

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:46.997053Z

Source-reported events for the cited work

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Observation af9db956-e1cd-4408-b520-88a212445871 · outbound

This paper cites Featured graph coarsening with similarity guarantees.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Featured graph coarsening with similarity guarantees

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.119011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d6f6aff-3d3a-4994-a8b6-1658e8e95f25 · outbound

This paper cites Partition-wise graph filtering: A unified perspective through the lens of graph coarsening.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Partition-wise graph filtering: A unified perspective through the lens of graph coarsening

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.250817Z

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Unavailable: canonical work link unavailable.

Observation 6ff322ce-1ad1-4ade-a483-0e9c9d1eefd2 · outbound

This paper cites Towards quantifying long-range interactions in graph machine learning: a large graph dataset and a measurement.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Towards quantifying long-range interactions in graph machine learning: a large graph dataset and a measurement

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.414221Z

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Observation da337f8f-19b3-4a06-b1d9-d800c971cf25 · outbound

This paper cites Graph reduction with spectral and cut guarantees.Journal of Machine Learning Research, 20(116):1–42, 2019.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Graph reduction with spectral and cut guarantees.Journal of Machine Learning Research, 20(116):1–42, 2019

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.583799Z

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Unavailable: canonical work link unavailable.

Observation f72ef6a6-4064-4dba-99d9-91f4bf346493 · outbound

This paper cites Classic gnns are strong baselines: Reassessing gnns for node classification.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Classic gnns are strong baselines: Reassessing gnns for node classification

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.689078Z

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Observation bf41b716-2fb9-4ffb-89b8-2fa9eacf6b24 · outbound

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

CoRe-GNN: Multilevel Message passing on Coarsened graphs A critical look at the evaluation of gnns under heterophily: Are we really making progress? InThe Eleventh International Conference on Learning Representations, 2023

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.811177Z

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Observation b9239952-36c7-436a-82dc-9b5b613bd7ca · outbound

This paper cites FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening.

CoRe-GNN: Multilevel Message passing on Coarsened graphs FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:47.924257Z

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Observation ef9f7f9e-a935-4933-ae56-03fb470ddaf8 · outbound

This paper cites Multi-scale attributed node embedding.Journal of Complex Networks, 9(2), 2021.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Multi-scale attributed node embedding.Journal of Complex Networks, 9(2), 2021

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:48.064556Z

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Observation a294722a-d24a-4f65-b352-230ff07bfbbf · outbound

This paper cites The graph neural network model.IEEE transactions on neural networks, 20(1):61–80, 2008.

CoRe-GNN: Multilevel Message passing on Coarsened graphs The graph neural network model.IEEE transactions on neural networks, 20(1):61–80, 2008

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:48.202799Z

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Observation e88e5c48-1ff3-4ef8-9fe5-29c7d96f0a3c · outbound

This paper cites Graph Attention Networks.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Graph Attention Networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:48.403409Z

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Observation 4985db1a-2756-48ac-b9dd-d544fc3c7a9d · outbound

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

CoRe-GNN: Multilevel Message passing on Coarsened graphs Next Level Message-Passing with Hierarchical Support Graphs

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:48.570023Z

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Observation 70af163a-9f2d-48f5-b0c4-6cf6ef8dde16 · outbound

This paper cites Simplifying graph convolutional networks.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Simplifying graph convolutional networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:48.766952Z

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Observation acd8fe58-8324-4736-8279-930dec46d034 · outbound

This paper cites Revisiting semi-supervised learning with graph embeddings.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Revisiting semi-supervised learning with graph embeddings

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:48.939112Z

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Observation 9b5d3f68-2e47-4302-8d16-502af8340d7d · outbound

This paper cites Hierarchical graph representation learning with differentiable pooling.Advances in neural information processing systems, 31, 2018.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Hierarchical graph representation learning with differentiable pooling.Advances in neural information processing systems, 31, 2018

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:49.120606Z

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Observation 86407185-c58a-433d-a023-7516b0ac4bb5 · outbound

This paper cites GraphSAINT: Graph Sampling Based Inductive Learning Method.

CoRe-GNN: Multilevel Message passing on Coarsened graphs GraphSAINT: Graph Sampling Based Inductive Learning Method

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:49.212802Z

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Observation 45676200-3f63-4378-87dd-aa5a69c80792 · outbound

This paper cites Decoupling the depth and scope of graph neural networks.Advances in neural information processing systems, 34:19665–19679, 2021.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Decoupling the depth and scope of graph neural networks.Advances in neural information processing systems, 34:19665–19679, 2021

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:49.330526Z

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Observation 569752ca-1109-4a00-938f-18b73b4afa62 · outbound

This paper cites Hierarchical message-passing graph neural networks: Z.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Hierarchical message-passing graph neural networks: Z

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:49.446236Z

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Unavailable: canonical work link unavailable.

Observation 8aef6835-e1a9-4a69-9acb-630b6c48ea63 · outbound

This paper cites Layer-dependent importance sampling for training deep and large graph convolutional networks.Advances in neural information processing systems, 32, 2019.

CoRe-GNN: Multilevel Message passing on Coarsened graphs Layer-dependent importance sampling for training deep and large graph convolutional networks.Advances in neural information processing systems, 32, 2019

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:49.589630Z

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Pith citing papers

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