A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation | Paper Citation Record

Observation ea25f641-edd7-4f9d-bce1-1eeccd82342b · outbound

This paper cites Trees and amenable equivalence relations.Ergodic Theory and Dynamical Systems, 10(1):1–14, 1990.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Trees and amenable equivalence relations.Ergodic Theory and Dynamical Systems, 10(1):1–14, 1990

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.363674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf7be0ce-2143-4fba-9b0c-11595a9f599d · outbound

This paper cites Springer, 2005.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Springer, 2005

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.367309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7961f17-41eb-4f16-948b-297891cede29 · outbound

This paper cites Characterizing the expressive power of invariant and equivariant graph neural networks.International Conference on Learning Representations, 2021.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Characterizing the expressive power of invariant and equivariant graph neural networks.International Conference on Learning Representations, 2021

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.370086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14e5c56a-798d-401e-a665-69d4d63b5fde · outbound

This paper cites Action convergence of operators and graphs.Canadian Journal of Mathematics, 74(1):72–121, 2022.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Action convergence of operators and graphs.Canadian Journal of Mathematics, 74(1):72–121, 2022

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.373793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60915b35-ce73-43f4-8e85-062902814264 · outbound

This paper cites Emergence of scaling in random networks.Science, 286(5439):509–512, 1999.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Emergence of scaling in random networks.Science, 286(5439):509–512, 1999

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.376604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e1960ac-da05-4ebb-8d06-10013d51141b · outbound

This paper cites Probability and measure.A Wiley-Interscience Publica- tion, John Wiley, 118:119, 1995.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Probability and measure.A Wiley-Interscience Publica- tion, John Wiley, 118:119, 1995

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.379564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67e6f560-815c-4f68-92e0-f09e0440207c · outbound

This paper cites Springer, 2007.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Springer, 2007

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.382343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 840da83d-5666-4eea-aa9a-a4638719587b · outbound

This paper cites Fine-grained expressivity of graph neural networks.Advances in Neural Information Processing Systems, 36, 2024.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Fine-grained expressivity of graph neural networks.Advances in Neural Information Processing Systems, 36, 2024

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.384882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7765b99-b944-4b34-b576-faaef1016fdf · outbound

This paper cites an unresolved cited work.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.387357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 069d9438-a5b7-4816-81c3-54bb867911c7 · outbound

This paper cites Sparse exchangeable graphs and their limits via graphon processes.Journal of Machine Learning Research, 18(210):1–71, 2018.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Sparse exchangeable graphs and their limits via graphon processes.Journal of Machine Learning Research, 18(210):1–71, 2018

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.389997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e54c7347-2565-42e5-ab51-1801bb548f1e · outbound

This paper cites An Lp theory of sparse graph convergence ii: Ld convergence, quotients and right convergence.The Annals of Probability, 2018.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation An Lp theory of sparse graph convergence ii: Ld convergence, quotients and right convergence.The Annals of Probability, 2018

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.392352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3e418ad-9cba-4f99-9b43-ebb4285d1738 · outbound

This paper cites Convergent sequences of dense graphs i: Subgraph frequencies, metric properties and testing.Advances in Mathematics, 219(6):1801–1851, 2008.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Convergent sequences of dense graphs i: Subgraph frequencies, metric properties and testing.Advances in Mathematics, 219(6):1801–1851, 2008

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.395453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cda60a8-a785-4ee4-9964-903b659c1fb2 · outbound

This paper cites Universal function approximation on graphs.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Universal function approximation on graphs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.397980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a4adbd0-9b85-43f0-898e-75a8ae331e27 · outbound

This paper cites American Mathematical Society Providence, 2001.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation American Mathematical Society Providence, 2001

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.400293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99753be4-f5cc-4398-8b92-69a03fb85dc4 · outbound

This paper cites Monte carlo and quasi-monte carlo methods.Acta numerica, 7:1–49, 1998.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Monte carlo and quasi-monte carlo methods.Acta numerica, 7:1–49, 1998

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.402756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e3e7fff-c939-40db-a16c-d6ed91c3f1db · outbound

This paper cites Machine learning on graphs: A model and comprehensive taxonomy.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Machine learning on graphs: A model and comprehensive taxonomy

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.405054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ffaf536-ccbe-4499-9739-3a103b6c6b09 · outbound

This paper cites Weisfeiler-lehman meets Gromov-Wasserstein.International Conference on Machine Learning, 2022.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Weisfeiler-lehman meets Gromov-Wasserstein.International Conference on Machine Learning, 2022

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.407538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c95a2b8-4cfa-43c6-897a-ad70a89e8a54 · outbound

This paper cites On the equiva- lence between graph isomorphism testing and function approximation with gnns.Advances in neural information processing systems, 3, 2019.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation On the equiva- lence between graph isomorphism testing and function approximation with gnns.Advances in neural information processing systems, 3, 2019

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.409781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10072545-6269-48bd-bc96-f2b2ea6a2899 · outbound

This paper cites Approximation by superpositions of a sigmoidal function.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Approximation by superpositions of a sigmoidal function

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.411888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e02670f-8efa-4f00-9348-f9bdc0014786 · outbound

This paper cites Graph neural networks for social recommendation.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Graph neural networks for social recommendation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.414439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd701745-5b02-47d4-9af5-5e9d56499c84 · outbound

This paper cites John Wiley & Sons, 1999.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation John Wiley & Sons, 1999

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.416769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3641ce7a-bbeb-488e-a963-f2be270288da · outbound

This paper cites Spaces in which sequences suffice.Fundamenta Mathematicae, 57(1):107–115, 1965.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Spaces in which sequences suffice.Fundamenta Mathematicae, 57(1):107–115, 1965

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.419342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bde7086-e08c-47ef-b297-95eb11363914 · outbound

This paper cites On the approximate realization of continuous mappings by neural networks.Neural networks, 2(3):183–192, 1989.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation On the approximate realization of continuous mappings by neural networks.Neural networks, 2(3):183–192, 1989

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.421765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40dc342e-71b8-41d0-aea7-da45ad557dd1 · outbound

This paper cites A survey of graph neural networks for recommender systems: Challenges, methods, and directions.ACM Transactions on Recommender Systems, 1(1):1–51, 2023.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation A survey of graph neural networks for recommender systems: Challenges, methods, and directions.ACM Transactions on Recommender Systems, 1(1):1–51, 2023

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.424011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8f10de7-eb28-4abf-a03c-a3da0d1f05b7 · outbound

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

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Generalization and representational limits of graph neural networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.427145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc254cbb-5b6c-420d-bc85-71e0d563d894 · outbound

This paper cites Expressiveness and approximation properties of graph neural networks.International Conference on Learning Representations, 2022.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Expressiveness and approximation properties of graph neural networks.International Conference on Learning Representations, 2022

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.429427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5e1a2cd-03d3-4239-9ecf-7d822b725dad · outbound

This paper cites Neural message passing for quantum chemistry.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Neural message passing for quantum chemistry

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.431637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f230ad0-cf03-450a-af25-1575244c3280 · outbound

This paper cites Fractional isomorphism of graphons.Combi- natorica, 42(3):365–404, 2022.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Fractional isomorphism of graphons.Combi- natorica, 42(3):365–404, 2022

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.434719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52e3fc9f-0512-470d-88d9-2b7b20485c89 · outbound

This paper cites word2vec, node2vec, graph2vec, x2vec: Towards a theory of vector embeddings of structured data.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation word2vec, node2vec, graph2vec, x2vec: Towards a theory of vector embeddings of structured data

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.437165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe277b3d-c30c-4bb6-8ac6-9c3af43c4cb4 · outbound

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

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Inductive representation learning on large graphs.Advances in neural information processing systems, 30, 2017

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.439398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2d1bc84-5090-43d3-80f6-6b7a194bf0dc · outbound

This paper cites American Mathe- matical Soc., 1978.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation American Mathe- matical Soc., 1978

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.441737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed8a67ea-6d9a-488c-8ea2-7607f98879b6 · outbound

This paper cites Limits of action convergent graph sequences with un- bounded (p, q)-norms, 2022.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Limits of action convergent graph sequences with un- bounded (p, q)-norms, 2022

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.444045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2802751-865b-4ed2-a112-adf484ef0431 · outbound

This paper cites Modeling Sparse Graph Sequences and Signals Using Generalized Graphons.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Modeling Sparse Graph Sequences and Signals Using Generalized Graphons

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.446338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3947e570-609f-4133-a277-c5e9ac4c0876 · outbound

This paper cites Generalized Graphon Process: Convergence of Graph Frequencies in Stretched Cut Distance.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Generalized Graphon Process: Convergence of Graph Frequencies in Stretched Cut Distance

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.448906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 285a1204-5d40-4ed6-b728-4f9102f037a8 · outbound

This paper cites Convolutional neural networks for image classification.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Convolutional neural networks for image classification

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.451505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfa006ad-ce64-4443-a974-c086d967c760 · outbound

This paper cites Universal invariant and equivariant graph neural networks.Advances in neural information processing systems, 32, 2019.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Universal invariant and equivariant graph neural networks.Advances in neural information processing systems, 32, 2019

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.453988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2183978b-65dd-49b5-9e5f-178488d854f3 · outbound

This paper cites Semi-supervised classification with graph convolutional networks.International Conference on Learning Representa- tions, 2017.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Semi-supervised classification with graph convolutional networks.International Conference on Learning Representa- tions, 2017

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.456456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9398841-2c14-4dd1-83c7-32dedf54672c · outbound

This paper cites Lecun, L.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Lecun, L

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.458847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54655982-e979-4506-a0fa-d3deb80be0e8 · outbound

This paper cites Multilayer feedforward networks with a nonpolynomial activation function can approximate any function.Neural networks, 6(6):861–867, 1993.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Multilayer feedforward networks with a nonpolynomial activation function can approximate any function.Neural networks, 6(6):861–867, 1993

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.461151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77ca4471-c937-410c-a421-812ef3fa8ce6 · outbound

This paper cites A graphon-signal analysis of graph neural networks.Advances in Neural Information Processing Systems, 36, 2024.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation A graphon-signal analysis of graph neural networks.Advances in Neural Information Processing Systems, 36, 2024

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.463443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 594e5231-1df9-422f-9734-6eac250da907 · outbound

This paper cites A pac-bayesian approach to generalization bounds for graph neural networks.International Conference on Learning Representations, 2021.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation A pac-bayesian approach to generalization bounds for graph neural networks.International Conference on Learning Representations, 2021

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.465908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcfc04f4-cfaf-43d7-a57d-aa8618122df6 · outbound

This paper cites American Mathematical Soc., 2012.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation American Mathematical Soc., 2012

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.468324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93c4db8c-692a-4d9c-94c2-21afd1d267d9 · outbound

This paper cites Szemer´ edi’s lemma for the analyst.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Szemer´ edi’s lemma for the analyst

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.470572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcdb79ed-e0ed-468f-8bc8-dd4ad816f010 · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229, 2021.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229, 2021

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.473031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34950f59-c086-4bad-85e7-8c33403476d3 · 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.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Generalization bounds for message passing networks on mixture of graphons.SIAM Journal on Mathematics of Data Science, 7(2):802–825, 2025

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.475436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f34e594d-2b0e-4863-bcd4-a79068fd449c · outbound

This paper cites Transferability of graph neural networks: an extended graphon approach.Applied and Computational Harmonic Analysis, 63:48–83, 2023.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Transferability of graph neural networks: an extended graphon approach.Applied and Computational Harmonic Analysis, 63:48–83, 2023

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.477630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fcfa0c8-4887-4ae8-ba1f-9d6c719b411d · outbound

This paper cites Generaliza- tion analysis of message passing neural networks on large random graphs.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Generaliza- tion analysis of message passing neural networks on large random graphs

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.480115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58136ef5-b7c3-44e6-9362-b744400986b6 · outbound

This paper cites WL meet VC.International Conference on Machine Learning, 2023.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation WL meet VC.International Conference on Machine Learning, 2023

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.482456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation caf7d40b-ebe6-4fdd-8ab3-cd94a26b0aa9 · outbound

This paper cites Weisfeiler and leman go neural: Higher-order graph neural networks.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Weisfeiler and leman go neural: Higher-order graph neural networks

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.484581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22fca7d2-ab3d-4ea3-b367-af36e956be30 · outbound

This paper cites Generalization, ex- pressivity, and universality of graph neural networks on attributed graphs.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Generalization, ex- pressivity, and universality of graph neural networks on attributed graphs

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.486976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3da357e-83f9-49f8-a99f-ffa80f8d0927 · outbound

This paper cites A note on graphon-signal analysis of graph neural networks, 2025.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation A note on graphon-signal analysis of graph neural networks, 2025

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.489319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef425aed-c89c-4674-8eb8-84154dc6509a · outbound

This paper cites McGraw-Hill, 1976.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation McGraw-Hill, 1976

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.491886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dfe81db-bcb6-4363-99c9-39ef65ffa83d · outbound

This paper cites Springer, 2015.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Springer, 2015

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.494147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7efef0ad-5c62-450f-954a-5fafe16b7fca · outbound

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

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation The vapnik– chervonenkis dimension of graph and recursive neural networks.Neural Networks, 108:248–259, 2018

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.496706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a264bbf-3812-4b77-804f-5fefaa36431e · outbound

This paper cites Unbalanced optimal transport, from theory to numerics.Handbook of Numerical Analysis, 24:407–471, 2023.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Unbalanced optimal transport, from theory to numerics.Handbook of Numerical Analysis, 24:407–471, 2023

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.499029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60f6ab56-ff80-4446-8edd-f36d88cf5899 · outbound

This paper cites Cambridge university press, 2014.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Cambridge university press, 2014

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.501580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcce3d3a-3e9d-4a04-ad73-1bb68376a2e9 · outbound

This paper cites American Mathematical Society, Providence, R.I, 2011.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation American Mathematical Society, Providence, R.I, 2011

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.504143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73108970-7c0f-4958-997a-d31df2510733 · outbound

This paper cites Covered forest: Fine-grained generalization analysis of graph neural networks.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Covered forest: Fine-grained generalization analysis of graph neural networks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.506769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e04b62e-8771-4145-a840-c5a224772be0 · outbound

This paper cites Survey on Generalization Theory for Graph Neural Networks.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Survey on Generalization Theory for Graph Neural Networks

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.509061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42be840d-f5bf-4561-8f61-1a9cde56f31c · outbound

This paper cites Springer, 2009.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Springer, 2009

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.511998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3fac593-f861-4dd6-ba3e-332d87ec48f7 · outbound

This paper cites Molecular contrastive learning of representations via graph neural networks.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Molecular contrastive learning of representations via graph neural networks

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.514422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6b977bf-083b-48e5-a61c-04b96c382784 · outbound

This paper cites Robustness and generalization.Machine learning, 86:391–423, 2012.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Robustness and generalization.Machine learning, 86:391–423, 2012

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.516683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 208e9917-931c-407a-9b36-353f1eac15ad · outbound

This paper cites How pow- erful are graph neural networks?International Conference of Learning Representations, 2019.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation How pow- erful are graph neural networks?International Conference of Learning Representations, 2019

Reference 63

Resolution: malformed identifier; no resolver link, observed 2026-08-03T03:20:37.518896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23f9ae19-ff76-4f2a-a319-0b3ab6ff9d1f · outbound

This paper cites an unresolved cited work.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Unresolved cited work

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.521655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e228cd98-115a-4a86-8259-b20c68b879ea · outbound

This paper cites A.2.4 Marginal Measures When a measure is defined on a product space, it often represents a joint distribution of a sequence of random variables.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation A.2.4 Marginal Measures When a measure is defined on a product space, it often represents a joint distribution of a sequence of random variables

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.524046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa858f41-95b6-44df-8d97-12fb0126689d · outbound

This paper cites an unresolved cited work.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.526720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac65d405-c78f-41ee-919a-fb010dbef9ac · outbound

This paper cites an unresolved cited work.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.529311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22bde41d-e28f-4543-8d62-692fa6e5cb24 · outbound

This paper cites Intuitively (and informally), µx can be interpreted as a measure supported on the level set {w∈ Ω |f (w) = x}.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Intuitively (and informally), µx can be interpreted as a measure supported on the level set {w∈ Ω |f (w) = x}

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.532164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e0fb859-ef6c-489c-b425-dae5722e7a3b · outbound

This paper cites an unresolved cited work.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.534930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d6e61c6-6bf7-422a-95b3-a834d9d23f24 · outbound

This paper cites Monte Carlo approximation.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Monte Carlo approximation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.537531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3822b0fc-13b8-43c6-8667-1a6b0ca88749 · outbound

This paper cites continuous node sets.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation continuous node sets

Reference 71

Resolution: malformed identifier; no resolver link, observed 2026-08-03T03:20:37.540027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acba6d04-a8ad-4bd7-bcf0-0a8bf673ff9c · outbound

This paper cites Denote byB(Ω) the set of allP-operators on Ω.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Denote byB(Ω) the set of allP-operators on Ω

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.543352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f2dce2b-2032-4446-9d88-a0470d8dc3ca · outbound

This paper cites Denote by Bp,q(Ω) the space of all P-operators with finite ( p, q) norm.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Denote by Bp,q(Ω) the space of all P-operators with finite ( p, q) norm

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.545951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 921a7585-46c3-451a-a0e5-bc1a291051b1 · outbound

This paper cites neighborhoods.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation neighborhoods

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.548560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14274652-8056-435d-9157-3c68f4f18feb · outbound

This paper cites Thediagonal marginalizationof ν, denoted by DMd(ν), is the Borel measure onR 2k obtained by marginalizing out the coordinatesy 2.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Thediagonal marginalizationof ν, denoted by DMd(ν), is the Borel measure onR 2k obtained by marginalizing out the coordinatesy 2

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.551531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 255e56bd-c1eb-4678-b3b9-67020d0f43e4 · outbound

This paper cites Explicitly, DMd(Sk,d) := n DMd(ν) ν∈S Td k,d o.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation Explicitly, DMd(Sk,d) := n DMd(ν) ν∈S Td k,d o

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.554085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd4dac45-6335-4cad-93b4-e57252ad63bd · outbound

This paper cites graphing.

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation graphing

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-03T03:20:37.558016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers