Paper Citation Record · LEDGER

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs

As of 19 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2607.21094.

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

pith.paper-citation-record.v1
2607.21094 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T08:34:21.585593Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

28 of 28 outbound references displayed

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verified fuzzy0
unresolved27
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malformed identifier1
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External citation measurements

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Outbound references

Observation 4f73eca9-a405-47c2-b180-215068bee891 · outbound

This paper cites Graph neural networks in recommender systems: a survey.ACM Computing Surveys, 55(5):1–37, 2022.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Graph neural networks in recommender systems: a survey.ACM Computing Surveys, 55(5):1–37, 2022

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:19.031438Z

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Observation 86b28588-e88c-4d34-a4dc-cf6fa59c57a5 · outbound

This paper cites Graph neural networks for social recommendation.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Graph neural networks for social recommendation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:19.159336Z

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Observation c97ca59d-9490-4562-9aee-2e68d18ae30b · outbound

This paper cites Graph convolutional networks for text classification.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Graph convolutional networks for text classification

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:19.223364Z

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Observation 88433f79-1927-45f2-a08b-638432cdde92 · outbound

This paper cites an unresolved cited work.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:19.296849Z

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Observation 88a373c5-4d0a-4e93-8518-9108f7a35ce8 · outbound

This paper cites Neural message passing for quantum chemistry.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Neural message passing for quantum chemistry

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:19.380916Z

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Observation addf6c10-f26a-4581-a1d7-5c5f16903964 · outbound

This paper cites Axiomatic attribution for deep networks.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Axiomatic attribution for deep networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:19.462167Z

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Observation 8df0fed7-fed5-4c0e-b45c-2fb736b4f32d · outbound

This paper cites Princeton University Press, 2015.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Princeton University Press, 2015

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:19.543952Z

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Observation f9e27a86-8510-4eeb-a8e4-20baf6231793 · outbound

This paper cites The many shapley values for model explanation.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs The many shapley values for model explanation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:19.624222Z

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Observation 1490e240-9834-460e-a285-99b23dbf7254 · outbound

This paper cites Explainability in graph neural networks: A taxonomic survey.IEEE transactions on pattern analysis and machine intelligence, 45(5):5782–5799, 2022.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Explainability in graph neural networks: A taxonomic survey.IEEE transactions on pattern analysis and machine intelligence, 45(5):5782–5799, 2022

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:19.707951Z

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Observation 0fa378f9-4b66-4c43-9e2f-c9dacce406c1 · outbound

This paper cites Gnnexplainer: Generating explanations for graph neural networks.Advances in neural information processing systems, 32, 2019.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Gnnexplainer: Generating explanations for graph neural networks.Advances in neural information processing systems, 32, 2019

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:19.768846Z

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Observation 22a2f2d6-4776-4f0c-a06e-1dd80e222b99 · outbound

This paper cites Parameter- ized explainer for graph neural network.Advances in neural information processing systems, 33:19620–19631, 2020.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Parameter- ized explainer for graph neural network.Advances in neural information processing systems, 33:19620–19631, 2020

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:19.873452Z

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Observation 99033ea0-853e-4160-868f-b97e9ef2b981 · outbound

This paper cites On explainability of graph neural networks via subgraph explorations.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs On explainability of graph neural networks via subgraph explorations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:19.964461Z

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Observation be485fbe-b208-4457-9266-1e0f0ff1480d · outbound

This paper cites Flowx: Towards explainable graph neural networks via message flows.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(7):4567–4578, 2023.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Flowx: Towards explainable graph neural networks via message flows.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(7):4567–4578, 2023

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:20.076053Z

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Observation 9c89c020-1b70-42e5-a7dd-6f1fd6ce98eb · outbound

This paper cites Explainability methods for graph convolutional neural networks.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Explainability methods for graph convolutional neural networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:20.219165Z

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Observation 23334b34-c1d1-4cfe-a5e3-54796f334f41 · outbound

This paper cites A value for n-person games.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs A value for n-person games

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:20.339595Z

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Observation 83a0224c-0110-4441-9901-3986eef54c19 · outbound

This paper cites Fast axiomatic attribution for neural networks.Advances in Neural Information Processing Systems, 34:19513–19524, 2021.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Fast axiomatic attribution for neural networks.Advances in Neural Information Processing Systems, 34:19513–19524, 2021

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:20.484906Z

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Observation 0b7e3823-857e-405c-9abd-28bac231c4cb · outbound

This paper cites On the expressive power of deep polynomial neural networks.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs On the expressive power of deep polynomial neural networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:20.693083Z

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Observation 6a0e0de9-1a8f-47a1-ab8f-2abf3ab9c068 · outbound

This paper cites P-nets: Deep polynomial neural networks.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs P-nets: Deep polynomial neural networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:20.795715Z

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Observation 5ede044b-ffc6-419a-b2fa-563f23ce712c · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.Advances in neural information processing systems, 29, 2016.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Convolutional neural networks on graphs with fast localized spectral filtering.Advances in neural information processing systems, 29, 2016

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:20.943656Z

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Observation 3438040c-332e-4dcc-b5fb-5b9db473c69e · outbound

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

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs How powerful are graph neural networks? In International Conference on Learning Representations, 2019

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:21.061798Z

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Observation 60496242-487b-4344-9dd3-45bb894972c7 · outbound

This paper cites Calculation of gauss quadrature rules.Mathematics of computation, 23(106):221–230, 1969.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Calculation of gauss quadrature rules.Mathematics of computation, 23(106):221–230, 1969

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:21.195204Z

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Observation 9b8bdd9e-6055-4918-8278-4da184a92151 · outbound

This paper cites Robust design with arbitrary distributions using gauss-type quadrature formula.Structural and Multidisciplinary Optimization, 39(3):227–243, 2009.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Robust design with arbitrary distributions using gauss-type quadrature formula.Structural and Multidisciplinary Optimization, 39(3):227–243, 2009

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:21.317735Z

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Observation e3fb7017-0189-4916-a90b-c21bf908fd5a · outbound

This paper cites GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:21.444225Z

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Observation 5d3ed831-259c-4a97-9e94-30a92fd62148 · outbound

This paper cites Towards robust fidelity for evaluating explainability of graph neural networks.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Towards robust fidelity for evaluating explainability of graph neural networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:21.499199Z

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Observation 76bfe305-5f4e-4803-8d5b-6faf60309c9c · outbound

This paper cites Cambridge university press, 2003.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Cambridge university press, 2003

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:21.541705Z

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Observation ab29ca4e-4e92-436a-8d08-a1b98407228e · outbound

This paper cites Moleculenet: a benchmark for molecular machine learning.Chemical science, 9(2):513–530, 2018.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Moleculenet: a benchmark for molecular machine learning.Chemical science, 9(2):513–530, 2018

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:21.578056Z

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Observation 2314ed1c-b8cb-4c8b-8670-094a10f72639 · outbound

This paper cites Derivation and validation of toxicophores for mutagenicity prediction.Journal of medicinal chemistry, 48(1):312–320, 2005.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs Derivation and validation of toxicophores for mutagenicity prediction.Journal of medicinal chemistry, 48(1):312–320, 2005

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T08:34:21.582199Z

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Observation 51cdd3b0-bd13-49ce-b2eb-f309dc15ca98 · outbound

This paper cites can”, “moving.

A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs can”, “moving

Reference 28

Resolution: malformed identifier; no resolver link, observed 2026-08-01T08:34:21.585593Z

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Pith citing papers

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