Paper Citation Record · LEDGER

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research

As of 8 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2506.04653.

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

pith.paper-citation-record.v1
2506.04653 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:44:01.831299Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

48 of 48 outbound references displayed

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verified fuzzy39
unresolved8
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 76e092d7-594b-4394-b719-714b35f411fe · outbound

This paper cites ChainerMN: Scalable Distributed Deep Learning Framework.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research ChainerMN: Scalable Distributed Deep Learning Framework

Reference 1

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Observation ca5ced3c-2e8a-49dc-9fd1-f028e8c29f0b · outbound

This paper cites On vanishing gradients, over- smoothing, and over-squashing in gnns: Bridging recurrent and graph learning.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research On vanishing gradients, over- smoothing, and over-squashing in gnns: Bridging recurrent and graph learning

Reference 2

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Observation 7ef74a88-4de5-4c16-bf14-3403268009d5 · outbound

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

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Beyond low-frequency information in graph convolutional networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:09.167796Z

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Observation c1be2840-8428-45cd-8307-ccd9fc5b8bd1 · outbound

This paper cites A note on over-smoothing for graph neural networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research A note on over-smoothing for graph neural networks

Reference 4

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Observation 574ec980-32cb-4605-b35e-6e5f3902db30 · outbound

This paper cites Grand: Graph neural diffusion.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Grand: Graph neural diffusion

Reference 5

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Observation eaca6802-1917-4e25-a866-64ab37a6a736 · outbound

This paper cites Simple and deep graph convolutional networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Simple and deep graph convolutional networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:08.708868Z

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Observation ec155a1f-fe92-42a3-9165-893bc5cce7b6 · outbound

This paper cites Long range graph benchmark.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Long range graph benchmark

Reference 7

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Observation 265eddcd-3320-4a96-af11-f1e7d55adbc9 · outbound

This paper cites PDE-GCN: Novel architectures for graph neural networks motivated by partial differential equations.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research PDE-GCN: Novel architectures for graph neural networks motivated by partial differential equations

Reference 8

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Observation 2c255ff3-b2f4-48c3-a771-344983d1917d · outbound

This paper cites Dropmes- sage: Unifying random dropping for graph neural networks.Proceedings of the AAAI Conference on Artificial Intelligence, 37(4):4267–4275, Jun.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Dropmes- sage: Unifying random dropping for graph neural networks.Proceedings of the AAAI Conference on Artificial Intelligence, 37(4):4267–4275, Jun

Reference 9

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Observation 495269bf-ab66-418f-98ff-dede56ecdfd3 · outbound

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The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Unresolved cited work

Reference 10

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Observation e7149c11-faf0-44f1-86f1-16d1ba1b203d · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Understanding the difficulty of training deep feedforward neural networks

Reference 11

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Observation 43e1bcca-f525-4710-8b70-332f80b992db · outbound

This paper cites Inductive representation learning on large graphs.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Inductive representation learning on large graphs

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:08.001010Z

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Observation db46330f-cdc6-44b2-9314-589d9776ab4a · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:07.858102Z

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Observation c422c020-5df9-4ec1-ab2b-f91966e51f62 · outbound

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

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Bernnet: Learning arbitrary graph spectral filters via bernstein approximation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:07.698741Z

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Observation e5b61d6e-a1d7-4568-910c-10c06414de59 · outbound

This paper cites Open Graph Benchmark: Datasets for Machine Learning on Graphs.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 15

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Observation 5b629470-89fc-4941-94ad-c3eb8d9caa3d · outbound

This paper cites Not too little, not too much: a theoretical analysis of graph (over)smoothing.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Not too little, not too much: a theoretical analysis of graph (over)smoothing

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:07.568988Z

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Observation 9d3c4e4b-6f64-4fa4-85aa-2a8f21e4c7f3 · outbound

This paper cites Kipf and Max Welling.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Kipf and Max Welling

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:07.442707Z

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Observation ff015c8c-625c-45d9-b4b8-2de1f7ce5e75 · outbound

This paper cites Deepgcns: Can gcns go as deep as cnns? In Proceedings of the IEEE/CVF international conference on computer vision, pages 9267–9276, 2019.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Deepgcns: Can gcns go as deep as cnns? In Proceedings of the IEEE/CVF international conference on computer vision, pages 9267–9276, 2019

Reference 18

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Observation 0a885a96-5942-4d61-a674-9455c2f5b1a4 · outbound

This paper cites Training graph neural networks with 1000 layers.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Training graph neural networks with 1000 layers

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:07.113805Z

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Observation 157b438b-48ad-4c30-af97-b894971a3e79 · outbound

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

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Deeper insights into graph convolutional networks for semi-supervised learning

Reference 20

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Observation 417efef6-14a2-4a52-af94-2cde04913880 · outbound

This paper cites Skipnode: On alleviating performance degradation for deep graph convolutional networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Skipnode: On alleviating performance degradation for deep graph convolutional networks

Reference 21

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Observation 01ab829d-1518-4329-9659-7cda1f636242 · outbound

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

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Classic GNNs are strong baselines: Reassessing GNNs for node classification

Reference 22

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Observation d485f88f-91bf-4fc8-b5ba-84e2d393a6cb · outbound

This paper cites Image-based recommendations on styles and substitutes.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Image-based recommendations on styles and substitutes

Reference 23

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Observation 353298e3-a129-47bb-aac2-f387ef10ab90 · outbound

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

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Graph neural networks exponentially lose expressive power for node classification

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:06.573439Z

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Observation 536f79f5-3785-4615-90bd-9922aa870453 · outbound

This paper cites Mitigating oversmoothing through reverse process of GNNs for heterophilic graphs.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Mitigating oversmoothing through reverse process of GNNs for heterophilic graphs

Reference 25

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Observation de28c197-f021-497d-b087-939cfd17db6f · outbound

This paper cites Geom-GCN: Geometric Graph Convolutional Networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Geom-GCN: Geometric Graph Convolutional Networks

Reference 26

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This paper cites Multi-track message passing: Tackling oversmoothing and oversquashing in graph learning via preventing heterophily mixing.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Multi-track message passing: Tackling oversmoothing and oversquashing in graph learning via preventing heterophily mixing

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:06.337966Z

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Observation 0f1399d2-afc1-449c-a9d9-aa5610123130 · outbound

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

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research A critical look at evaluation of gnns under heterophily: Are we really making progress? In The Eleventh International Conference on Learning Representations, 2023

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:06.148950Z

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Observation abebb8a6-90d3-4516-a917-9dd313c5b1b3 · outbound

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

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Dropedge: Towards deep graph convolutional networks on node classification

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:05.998010Z

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Observation 3c8769b7-1ced-4223-a606-f19e98f4e47b · outbound

This paper cites Multi-scale attributed node embedding.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Multi-scale attributed node embedding

Reference 30

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Observation cde7b7e4-c531-4417-9cf5-9ae56c8779b3 · outbound

This paper cites Graph-coupled oscillator networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Graph-coupled oscillator networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:05.651788Z

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Observation c956adcf-dcfe-4168-be24-b64834c92f96 · outbound

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

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research A Survey on Oversmoothing in Graph Neural Networks

Reference 32

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This paper cites Konstantin Rusch, Benjamin Paul Chamberlain, Michael W.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Konstantin Rusch, Benjamin Paul Chamberlain, Michael W

Reference 33

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This paper cites Collective classification in network data.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Collective classification in network data

Reference 34

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Observation e999fddd-15ac-4272-bf0e-03bb06f45e90 · outbound

This paper cites Ordered GNN: Ordering message passing to deal with heterophily and over-smoothing.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Ordered GNN: Ordering message passing to deal with heterophily and over-smoothing

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:05.199579Z

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This paper cites Simple and deep graph attention networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Simple and deep graph attention networks

Reference 36

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This paper cites Grand++: Graph neural diffusion with a source term.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Grand++: Graph neural diffusion with a source term

Reference 37

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This paper cites Chainer: a next-generation open source framework for deep learning.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Chainer: a next-generation open source framework for deep learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:04.504627Z

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This paper cites Chainer: A deep learning framework for accelerating the research cycle.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Chainer: A deep learning framework for accelerating the research cycle

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:04.119530Z

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This paper cites Visualizing data using t-sne.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Visualizing data using t-sne

Reference 40

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This paper cites Graph attention networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Graph attention networks

Reference 41

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This paper cites Simplifying graph convolutional networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Simplifying graph convolutional networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:03.600318Z

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Observation c2628bef-b24a-4b6f-a18f-14294c8a6a2f · outbound

This paper cites Demystifying oversmoothing in attention-based graph neural networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Demystifying oversmoothing in attention-based graph neural networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:03.250597Z

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Observation 0415241c-0cba-4a07-bed2-efc213e3b9be · outbound

This paper cites A non-asymptotic analysis of oversmoothing in graph neural networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research A non-asymptotic analysis of oversmoothing in graph neural networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:03.024509Z

Source-reported events for the cited work

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Observation 9692189c-6d21-47c0-9e5b-ab756eaac9a5 · outbound

This paper cites Model degradation hinders deep graph neural networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Model degradation hinders deep graph neural networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:02.870133Z

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Observation 9b9ea8d6-1ff5-4ed6-9b96-841b69ead489 · outbound

This paper cites Pairnorm: Tackling oversmoothing in gnns.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Pairnorm: Tackling oversmoothing in gnns

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:02.715681Z

Source-reported events for the cited work

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Observation 743a66b3-5747-4d3d-b3b1-b9765d824cb1 · outbound

This paper cites Dirichlet energy constrained learning for deep graph neural networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Dirichlet energy constrained learning for deep graph neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:02.578468Z

Source-reported events for the cited work

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Observation 8f6d37b7-ac41-421c-93f2-85915f22c349 · outbound

This paper cites Understanding and resolving performance degradation in deep graph convolutional networks.

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research Understanding and resolving performance degradation in deep graph convolutional networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:02.427359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.