Paper Citation Record · LEDGER

Learning from one graph: transductive learning guarantees via the geometry of small random worlds

As of 20 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 2 inbound Pith citation observations for arXiv:2509.06894.

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

pith.paper-citation-record.v1
2509.06894 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

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measured 70 of 70 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:47:38.908007Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

68 of 68 outbound references displayed

verified exact5
verified fuzzy52
unresolved11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9ce0efa7-76e5-4304-b26c-eb9fae4f3fff · outbound

This paper cites Zero-one laws of graph neural networks.Advances in Neural Information Processing Systems, 36:70733–70756, 2023.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Zero-one laws of graph neural networks.Advances in Neural Information Processing Systems, 36:70733–70756, 2023

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.353708Z

Source-reported events for the cited work

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Observation 4d7f1e04-3e09-45fc-85b2-49c788571b80 · outbound

This paper cites Stronger generalization bounds for deep nets via a compression approach.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Stronger generalization bounds for deep nets via a compression approach

Reference 2

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Observation e2431f5f-7a4b-447c-b038-45146afc3533 · outbound

This paper cites Plongements Lipschitziens dansRn.Bulletin de la Société Mathématique de France, 111:429–448, 1983.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Plongements Lipschitziens dansRn.Bulletin de la Société Mathématique de France, 111:429–448, 1983

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.328682Z

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Observation d75e1e7c-c488-48a1-9d91-710483e569b2 · outbound

This paper cites High-dimensional analysis of double descent for linear regression with random projections.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds High-dimensional analysis of double descent for linear regression with random projections

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.313865Z

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Observation d8595e82-831e-4863-81f3-56ffd03969d7 · outbound

This paper cites Failures of model-dependent generalization bounds for least-norm interpolation.Journal of Machine Learning Research, 22(204):1–15, 2021.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Failures of model-dependent generalization bounds for least-norm interpolation.Journal of Machine Learning Research, 22(204):1–15, 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.298982Z

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Observation de3f21d7-a29c-41a9-a150-93192864e43a · outbound

This paper cites Two models of double descent for weak features.SIAM Journal on Mathematics of Data Science, 2(4):1167–1180, 2020.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Two models of double descent for weak features.SIAM Journal on Mathematics of Data Science, 2(4):1167–1180, 2020

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T23:03:48.150593Z

Source-reported events for the cited work

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Observation b1dc0968-662c-425f-9b2e-0855dbe7a55d · outbound

This paper cites Cambridge University Press, Cambridge, second edition, 2001.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Cambridge University Press, Cambridge, second edition, 2001

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.271627Z

Source-reported events for the cited work

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Observation 4a54e51f-37bb-4cce-9176-c8954737292e · outbound

This paper cites Compositional PAC-bayes: Generalization of GNNs with persistence and beyond.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Compositional PAC-bayes: Generalization of GNNs with persistence and beyond

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.256052Z

Source-reported events for the cited work

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Observation 62489fa9-e7da-4349-a648-0dd7b73f442e · outbound

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

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T23:03:48.359358Z

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Observation 48c00646-fce1-46ba-bccf-0c25eef81c52 · outbound

This paper cites Tony Cai and Mark G.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Tony Cai and Mark G

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.240664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 516ecccd-5418-4336-ae0e-5f3a4cf85f26 · outbound

This paper cites Mean field games with common noise.The Annals of Probability, 44(6):3740–3803, 2016.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Mean field games with common noise.The Annals of Probability, 44(6):3740–3803, 2016

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.226106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 346aa5b1-fdda-48e5-a9bc-071b9233364e · outbound

This paper cites Connected components in random graphs with given expected degree sequences.Annals of Combinatorics, 6(2):125–145, 2002.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Connected components in random graphs with given expected degree sequences.Annals of Combinatorics, 6(2):125–145, 2002

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.211373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 026d37ef-b3a5-4a63-b049-3a3f8666b270 · outbound

This paper cites Das and Pawan Kumar.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Das and Pawan Kumar

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.194527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0d7672fa-38d0-41fd-b8fb-f947f688a937 · outbound

This paper cites A non-probabilistic proof of the Assouad embedding theorem with bounds on the dimension.Analysis and Geometry in Metric Spaces, 1(2013):36–41, 2013.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds A non-probabilistic proof of the Assouad embedding theorem with bounds on the dimension.Analysis and Geometry in Metric Spaces, 1(2013):36–41, 2013

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.176904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 289969b8-1483-4617-b4e1-d69eaa546865 · outbound

This paper cites McKean-Vlasov optimal control: the dynamic programming principle.The Annals of Probability, 50(2):791–833, 2022.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds McKean-Vlasov optimal control: the dynamic programming principle.The Annals of Probability, 50(2):791–833, 2022

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.161412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 80c46952-2392-47e5-b760-7a3663fb46f1 · outbound

This paper cites Doublingconstantsandspectraltheory on graphs.Discrete Mathematics, 346(6):Paper No.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Doublingconstantsandspectraltheory on graphs.Discrete Mathematics, 346(6):Paper No

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.144204Z

Source-reported events for the cited work

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Observation b3c30f81-df46-46e7-a35e-4c704829b11d · outbound

This paper cites Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

Reference 17

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Observation 04633fb7-6b94-42d2-becf-0763fdb4ba7a · outbound

This paper cites On the approximation capability of gnns in node classification/regression tasks.Soft Computing, 28(13):8527– 8547, 2024.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds On the approximation capability of gnns in node classification/regression tasks.Soft Computing, 28(13):8527– 8547, 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.129365Z

Source-reported events for the cited work

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Observation 68d0a97e-41e4-4c9c-916c-a46bda11ea6f · outbound

This paper cites Transductive Rademacher complexity and its applications.Journal of Artificial Intelligence Research, 35:193–234, 2009.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Transductive Rademacher complexity and its applications.Journal of Artificial Intelligence Research, 35:193–234, 2009

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.114050Z

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Observation 72cdd9cb-c090-4109-8f29-8138756dc2ee · outbound

This paper cites On the rate of convergence in Wasserstein distance of the empirical measure.Probability Theory and Related Fields, 162(3):707–738, 2015.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds On the rate of convergence in Wasserstein distance of the empirical measure.Probability Theory and Related Fields, 162(3):707–738, 2015

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T23:03:49.262331Z

Source-reported events for the cited work

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Observation d1422f8e-2256-4435-ae89-937da80d2bf9 · outbound

This paper cites Generalization and representational limits of graph neural networks.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Generalization and representational limits of graph neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.087204Z

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Observation 4f50d748-aac2-44a4-a019-f6885d52b39a · outbound

This paper cites Fast construction of nets in low-dimensional metrics and their applications.SIAM Journal on Computing, 35(5):1148–1184, 2006.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Fast construction of nets in low-dimensional metrics and their applications.SIAM Journal on Computing, 35(5):1148–1184, 2006

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.072223Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 97b9456f-a8ef-4c35-abea-ebe4884f1041 · outbound

This paper cites Universitext.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Universitext

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.056335Z

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Observation 3aef1010-56ec-4d04-9a4f-2ff7ae1fcbf8 · outbound

This paper cites Cambridge university press, 2012.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Cambridge university press, 2012

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T23:03:49.580710Z

Source-reported events for the cited work

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Observation 05420ebe-4b46-475a-9c89-26d32543fc59 · outbound

This paper cites Instance- dependent generalization bounds via optimal transport.Journal of Machine Learning Research, 24(349):1–51, 2023.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Instance- dependent generalization bounds via optimal transport.Journal of Machine Learning Research, 24(349):1–51, 2023

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.030798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5e3ffd45-ec2b-4c3a-8a2f-a8723e9ed07f · outbound

This paper cites Big Data + Big Cities: Graph Signals of Urban Air Pollution [Exploratory Sp].IEEE Signal Processing Magazine, 31(5):130–136, 2014.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Big Data + Big Cities: Graph Signals of Urban Air Pollution [Exploratory Sp].IEEE Signal Processing Magazine, 31(5):130–136, 2014

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.016139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2b35e836-e5f2-4f60-b75c-c369cf250655 · outbound

This paper cites Practical graph signal sampling with log-linear size scaling.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Practical graph signal sampling with log-linear size scaling

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:56.001156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1799fa2d-22de-4956-81ff-eef9d69eccc2 · outbound

This paper cites Prediction of protein–protein interaction using graph neural networks.Scientific Reports, 12(1):8360, 2022.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Prediction of protein–protein interaction using graph neural networks.Scientific Reports, 12(1):8360, 2022

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.985839Z

Source-reported events for the cited work

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Observation 0af47754-ad6a-4fa2-9b67-30b8a9623b52 · outbound

This paper cites Minimax estimation of functionals of discrete distributions.IEEE Transactions on Information Theory, 61(5):2835–2885, 2015.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Minimax estimation of functionals of discrete distributions.IEEE Transactions on Information Theory, 61(5):2835–2885, 2015

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.968971Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4bd964c5-b2cb-4dd5-9f84-6e2adbecf2fd · outbound

This paper cites Personalized Explanations for Early Diagnosis of Alzheimer’s Disease Using Explainable Graph Neural Networks with Population Graphs.Bioengineering, 10(6):701, 2023.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Personalized Explanations for Early Diagnosis of Alzheimer’s Disease Using Explainable Graph Neural Networks with Population Graphs.Bioengineering, 10(6):701, 2023

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.953680Z

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Observation ac95b368-707a-47e2-9ed5-be5c225b5242 · outbound

This paper cites Kipf and Max Welling.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Kipf and Max Welling

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.938499Z

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Observation 52d7c269-a61e-481e-9cad-9e5f09a3694d · outbound

This paper cites Kloeckner.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Kloeckner

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.923754Z

Source-reported events for the cited work

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Observation c4f5a8c2-58b3-4038-ad4d-9ebddc4094db · outbound

This paper cites Exactlowerboundsfortheagnosticprobably-approximately-correct (PAC) machine learning model.The Annals of Statistics, 47(5):2822–2854, 2019.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Exactlowerboundsfortheagnosticprobably-approximately-correct (PAC) machine learning model.The Annals of Statistics, 47(5):2822–2854, 2019

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.907582Z

Source-reported events for the cited work

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Observation d6729c64-240b-4f4b-8f22-215d31a2a614 · outbound

This paper cites Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-04T23:03:54.556002Z

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Observation 49334daf-2beb-407b-aa0f-a43d3c1115a2 · outbound

This paper cites Learning skillful medium-range global weather forecasting.Science, 382(6677):1416–1421, 2023.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Learning skillful medium-range global weather forecasting.Science, 382(6677):1416–1421, 2023

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T23:03:50.544321Z

Source-reported events for the cited work

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Observation 333e1b52-7519-4b2f-8797-aaff6c087901 · outbound

This paper cites Lepski, A.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Lepski, A

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.883179Z

Source-reported events for the cited work

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Observation bc77ca5e-f16c-4a44-883f-981c54dfe10e · outbound

This paper cites A graphon-signal analysis of graph neural networks.Advances in Neural Information Pro- cessing Systems, 36:64482–64525, 2023.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds A graphon-signal analysis of graph neural networks.Advances in Neural Information Pro- cessing Systems, 36:64482–64525, 2023

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.863602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 420e43b3-472b-47af-b2bf-b241dd31891a · outbound

This paper cites A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-04T23:03:54.363940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8493292e-4d3d-41e2-9f05-624611d2602e · outbound

This paper cites Lorentz, Manfred v.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Lorentz, Manfred v

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.847138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a7101320-1401-4fab-ada7-45bb9a62cc59 · outbound

This paper cites Cambridge University Press, 2021.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Cambridge University Press, 2021

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.826332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f769c981-9ab7-49ea-9beb-edab26c62736 · outbound

This paper cites Generalization bounds for message passing networks on mixture of graphons.SIAM Journal on Mathematics of Data Science, 7(2):802–825, 2025.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Generalization bounds for message passing networks on mixture of graphons.SIAM Journal on Mathematics of Data Science, 7(2):802–825, 2025

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.809223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 429e2bcc-3a61-4869-9d83-4fd1e1eb9aeb · outbound

This paper cites Bi-lipschitz embeddings into low-dimensional euclidean spaces.Commentationes Math- ematicae Universitatis Carolinae, 031(3):589–600, 1990.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Bi-lipschitz embeddings into low-dimensional euclidean spaces.Commentationes Math- ematicae Universitatis Carolinae, 031(3):589–600, 1990

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.792303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e5ca0dc1-3e93-4de5-9e3f-566e8f99d67b · outbound

This paper cites Springer- Verlag, New York, 2002.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Springer- Verlag, New York, 2002

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.775291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation be4f7880-b889-49bd-a5da-5fff78617aa9 · outbound

This paper cites When and why are deep networks better than shallow ones? InProceedings of the AAAI conference on artificial intelligence, volume 31, 2017.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds When and why are deep networks better than shallow ones? InProceedings of the AAAI conference on artificial intelligence, volume 31, 2017

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.757946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 261c5455-73a9-40a1-97fb-9d734f9a571e · outbound

This paper cites Assouad’s theorem with dimension independent of the snowflaking.Revista Matematica Iberoamericana, 28(4):1123–1142, 2012.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Assouad’s theorem with dimension independent of the snowflaking.Revista Matematica Iberoamericana, 28(4):1123–1142, 2012

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.740288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ab723e28-dbeb-42a1-b8ce-31cab430387a · outbound

This paper cites Low dimensional embeddings of doubling metrics.Theory Comput.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Low dimensional embeddings of doubling metrics.Theory Comput

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.721201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 41feeed3-2d92-46a4-a807-dee08500ee1c · outbound

This paper cites an unresolved cited work.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-04T23:03:55.703045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation baec5450-74fa-49c3-85ad-ecb7f4820b39 · outbound

This paper cites an unresolved cited work.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-04T23:03:55.683826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f466ec3b-a711-46d8-9523-7b919b6c1486 · outbound

This paper cites Fake news detection: A survey of graph neural network methods.Applied Soft Computing, 139:110235, 2023.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Fake news detection: A survey of graph neural network methods.Applied Soft Computing, 139:110235, 2023

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.664947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d63dbf1-528c-4bce-ba43-fa19f69c88aa · outbound

This paper cites Real analysis, 4th edition.Printice-Hall Inc, Boston, 2010.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Real analysis, 4th edition.Printice-Hall Inc, Boston, 2010

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.637580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 372ae754-db67-41a7-ade0-46cdaf26ae34 · outbound

This paper cites The Vapnik-Chervonenkis dimension of graph and recursive neural networks.Neural Networks, 108:248–259, 2018.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds The Vapnik-Chervonenkis dimension of graph and recursive neural networks.Neural Networks, 108:248–259, 2018

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.621951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7f272c5b-180f-49aa-b59b-97dd935995d2 · outbound

This paper cites Metric spaces and completely monotone functions.Annals of Mathematics, 39(4):811–841, 1938.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Metric spaces and completely monotone functions.Annals of Mathematics, 39(4):811–841, 1938

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.605775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8b2da571-279a-46d5-a112-258f37ff7649 · outbound

This paper cites Cambridge university press, 2014.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Cambridge university press, 2014

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-04T23:03:52.320206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19fcc69c-aa2f-4e5a-9aa9-90e0037251fc · outbound

This paper cites Homophily modulates double descent generalization in graph convolution networks.Proceedings of the National Academy of Sciences, 121(8):e2309504121, 2024.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Homophily modulates double descent generalization in graph convolution networks.Proceedings of the National Academy of Sciences, 121(8):e2309504121, 2024

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.578734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 431f9173-0c98-47ea-9b60-0d5e607ae817 · outbound

This paper cites The least doubling constant of a metric measure space.Annales Fennici Mathematici, 44(2):1015–1030, 2019.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds The least doubling constant of a metric measure space.Annales Fennici Mathematici, 44(2):1015–1030, 2019

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.562313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 00f722a1-8d19-45db-8ca7-a0dcc1a014c1 · outbound

This paper cites Bronstein.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Bronstein

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.545372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 24e2cd5b-9915-48b9-ab26-e626798f3b18 · outbound

This paper cites Information-Theoretic Generalization Bounds for Transductive Learning and its Applications.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Information-Theoretic Generalization Bounds for Transductive Learning and its Applications

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-04T23:03:54.173785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e61994d6-7960-4290-8c29-cd0ebdf336d4 · outbound

This paper cites Weak convergence.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Weak convergence

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.526927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cf7fa15a-2f77-490c-8cd0-71b1d10acba9 · outbound

This paper cites Estimation of dependences based on empirical data: Springer series in statistics (springer series in statistics), 1982.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Estimation of dependences based on empirical data: Springer series in statistics (springer series in statistics), 1982

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.507251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 100543af-2ee3-43b3-81a7-f11105ccff8a · outbound

This paper cites Springer, 2009.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Springer, 2009

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-04T23:03:52.919362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e76f1533-f564-43fe-a3b1-46eb3029ad79 · outbound

This paper cites Recommending related products using graph neural networks in directed graphs.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Recommending related products using graph neural networks in directed graphs

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.480268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3a947827-f1c2-41cf-a024-a1b223dc7dcf · outbound

This paper cites Machining feature process route planning based on a graph convolutional neural network.Advanced Engineering Informatics, 59:102249, 2024.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Machining feature process route planning based on a graph convolutional neural network.Advanced Engineering Informatics, 59:102249, 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.463473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6909124d-0718-4d87-b4bb-ea1db4ee478a · outbound

This paper cites Sharp generalization of transductive learning: A transductive local Rademacher com- plexity approach.arXiv preprint arXiv:2309.16858, 2023.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Sharp generalization of transductive learning: A transductive local Rademacher com- plexity approach.arXiv preprint arXiv:2309.16858, 2023

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-08-04T23:03:53.973745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation beb6652a-3fc2-4445-bd65-99edc33bc50f · outbound

This paper cites Corner Gradient Descent.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Corner Gradient Descent

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-08-04T23:03:53.796280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 58bdb7eb-47f8-43ae-9f5c-1395f98b7e1c · outbound

This paper cites The phase diagram of approximation rates for deep neural networks.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds The phase diagram of approximation rates for deep neural networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.329715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9b2d6eae-9022-451a-aa86-e3bf16c18408 · outbound

This paper cites Strong data processing inequalities for locally differentially private mechanisms.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Strong data processing inequalities for locally differentially private mechanisms

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:55.164453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 173f16a0-f079-4a15-af35-0c5b54fc556e · outbound

This paper cites Link prediction based on graph neural networks.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Link prediction based on graph neural networks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:54.951294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 47a80999-4961-44ec-a418-d1e9e6aa8b69 · outbound

This paper cites Dgcn: Diversified recommendation with graph convolutional networks.

Learning from one graph: transductive learning guarantees via the geometry of small random worlds Dgcn: Diversified recommendation with graph convolutional networks

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:03:54.774529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 68c18e68-5446-44da-ad3b-aa185ac2bf39 · inbound

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures cites this paper.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Learning from one graph: transductive learning guarantees via the geometry of small random worlds

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T16:47:38.908007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b955126c-5e27-48ca-8741-b846807567f3 · inbound

Statistical Guarantees for Reasoning Probes on Looped Boolean Circuits cites this paper.

Statistical Guarantees for Reasoning Probes on Looped Boolean Circuits Learning from one graph: transductive learning guarantees via the geometry of small random worlds

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T05:00:58.184360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.