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Paper Citation Record · LEDGER

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks

As of 7 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 0 inbound Pith citation observations for arXiv:2508.14338.

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

pith.paper-citation-record.v1
2508.14338 v1

Coverage vector

measured 79 of 79 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:41:21.823852Z

measured 79 of 79 standing notices

One-hop event checks from named stored sources.

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

79 of 79 outbound references displayed

  • verified exact3
  • verified fuzzy55
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 877bfa7f-2429-488b-ab34-021d5341b98e · outbound

This paper cites A convergence analysis of gradient descent on graph neural networks.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks A convergence analysis of gradient descent on graph neural networks

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:14.484549Z digest=sha256:ba6d571b759ec2b280ed7d55bb1c20abb0737b329d5174be1f6c0eb0c26a337a

Observation a385dfdf-efd8-4ffc-8ad8-6b1d615d9b14 · outbound

This paper cites and Moulines, E.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Moulines, E

Reference 2

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raw_fallback, observed 2026-08-05T18:41:34.846326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:14.580166Z digest=sha256:836bf0b01aeafbeae2d5afa23721277df1033a344e5fb495d28eaf6b1a9a2a6b

Observation 1230ade7-1b76-48ac-be07-9e5727a48f9f · outbound

This paper cites Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization

Reference 3

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no resolver link, observed 2026-08-05T18:41:14.715907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:14.715907Z digest=sha256:e78148f190d07769cf60bb55bb032c8e208ec9ddc9a8d96fe102ad5a8ed470fe

Observation fad8f795-04a9-402b-aadc-3782839576f7 · outbound

This paper cites L., Long, P.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks L., Long, P

Reference 4

Resolution
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raw_fallback, observed 2026-08-05T18:41:34.493383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:14.876894Z digest=sha256:7773b859926bb72bf451431197974c6172ed622ba4ba6de96a0916b534f97772

Observation 9ebb4865-9041-48f6-94fe-d1fb26f3b1cb · outbound

This paper cites Tight nonparametric convergence rates for stochastic gradient descent under the noiseless linear model.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Tight nonparametric convergence rates for stochastic gradient descent under the noiseless linear model

Reference 5

Resolution
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raw_fallback, observed 2026-08-05T18:41:34.175493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:15.032112Z digest=sha256:5137163e740aae92b3e116c27d9df247f9c32aef46c0e207c5eb52bbf32fd5ac

Observation 04a591c0-3f7c-46f5-a2d2-17de600c2895 · outbound

This paper cites Stochastic Training of Graph Convolutional Networks with Variance Reduction.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Stochastic Training of Graph Convolutional Networks with Variance Reduction

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-05T18:41:15.153047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:15.153047Z digest=sha256:86f8da1cf34a9688581cdb9153ed60d234e1d58b5c2f5d3862ec358d7bad0c5d

Observation 185c7349-b403-46bb-9a24-792aa7a59f05 · outbound

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

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

Reference 7

Resolution
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no resolver link, observed 2026-08-05T18:41:15.327172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:15.327172Z digest=sha256:869570cb090b8cabf613db07c221d218d29f970c93386c14afc92ceb68eb9052

Observation 8b4a27b4-4404-4ef2-85c4-c78c9f682df3 · outbound

This paper cites Eigenvalues of random power law graphs.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Eigenvalues of random power law graphs

Reference 8

Resolution
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raw_fallback, observed 2026-08-05T18:41:33.881089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:15.497079Z digest=sha256:c839bcdf3f2761d59facc39b19074c7cf35d30a7b3764dcb8b3695f30192d214

Observation 66b655b4-dad6-4cdd-b998-4561a2d3022a · outbound

This paper cites an unresolved cited work.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-05T18:41:33.615845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:15.653112Z digest=sha256:2c7d8e31b46379241d6f0235d541b173a3454b3a7896ef93bba596ab62bdeb30

Observation fab6d47b-5c5a-4623-8570-0f747887d238 · outbound

This paper cites and Bach, F.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Bach, F

Reference 10

Resolution
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raw_fallback, observed 2026-08-05T18:41:33.337574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:15.818499Z digest=sha256:9f9c964811c7f003d6983b73ab4676e0f746b52c00cc6d1a3de97356f6d57ac6

Observation ec312054-a9b4-4af7-a8e4-abbbec563376 · outbound

This paper cites S., Foster, D.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks S., Foster, D

Reference 11

Resolution
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raw_fallback, observed 2026-08-05T18:41:33.201762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:15.944496Z digest=sha256:03e635cccd004d4fb02d73477d19330f4bcce4ecc72b02155e1b5bc274c400f1

Observation 427fd1b6-e7d5-4125-bbec-91691c6f32a3 · outbound

This paper cites Harder, better, faster, stronger convergence rates for least-squares regression.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Harder, better, faster, stronger convergence rates for least-squares regression

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:32.996006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:16.076181Z digest=sha256:5459fac23aad15497f0979d2ba7383765a31148482560096444612d6e1098740

Observation 3767df2a-7e04-41c8-bb0b-165511b99872 · outbound

This paper cites and Wager, S.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Wager, S

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:32.847541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c0410d9e-691a-4225-a9c2-0bdca5a659bf · outbound

This paper cites S., Hou, K., Salakhutdinov, R.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks S., Hou, K., Salakhutdinov, R

Reference 14

Resolution
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raw_fallback, observed 2026-08-05T18:41:32.678288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:16.275227Z digest=sha256:259ff66ecd31082f482e820ae108d48225868d66a5b672812a5b0068ee8900d4

Observation 8d2d2d94-0ab9-4e5d-92d9-ba027b1ceec4 · outbound

This paper cites Networks, crowds, and markets, volume 8.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Networks, crowds, and markets, volume 8

Reference 15

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raw_fallback, observed 2026-08-05T18:41:32.556885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:16.401466Z digest=sha256:0cea1304f7ad79a1239ad4e457cc8783ddaf94b746cfecc02223e832e02b05c4

Observation 7d28f397-d68c-4c7b-893c-cfad77703a47 · outbound

This paper cites On power-law relationships of the internet topology.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks On power-law relationships of the internet topology

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:32.411426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:16.499557Z digest=sha256:18285a3cd6dbe9f850cb7450083916571d1c238f74888944d6874072582bb705

Observation 993218e3-1908-4187-b057-40f7c8665233 · outbound

This paper cites real-world.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks real-world

Reference 17

Resolution
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raw_fallback, observed 2026-08-05T18:41:32.230214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:16.607280Z digest=sha256:8a88b412de78ab69a7d15501c199ef64c674daa7151dc51ade6deac47f5f447e

Observation 519f79b1-154e-4030-ba11-a149788e31f6 · outbound

This paper cites Community detection in graphs.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Community detection in graphs

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:32.024617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:16.725554Z digest=sha256:dec671b479b35e14910c142bcf48b6b068865b928754ee3800ffbb5614a233bd

Observation 7496bb54-4448-4540-b661-87ec1596dc5b · outbound

This paper cites Identifying network structure similarity using spectral graph theory.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Identifying network structure similarity using spectral graph theory

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:31.823040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:16.813231Z digest=sha256:1c62ac3112a62e38dffed6e84e6b3e348802e44fc121bb73f58b2d163285fc0d

Observation 1b9c71ff-4436-4677-94ad-5193bf4ec67d · outbound

This paper cites S., Riley, P.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks S., Riley, P

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:31.634581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:16.928402Z digest=sha256:7fc58d9ba6ecd86dd763accbd88582a195995c08a1ea8d6234620a6aa84e63d3

Observation 00dc4c8f-6477-4824-8c8e-d21c23bb749d · outbound

This paper cites Spectra and eigenvectors of scale-free networks.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Spectra and eigenvectors of scale-free networks

Reference 21

Resolution
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raw_fallback, observed 2026-08-05T18:41:31.442505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.002739Z digest=sha256:5aaf4d2c76a630948223cf146d9faa8e27ac1af856ad7ca9880dc2bfa478d28b

Observation 643d31ff-8347-45fc-8115-ae545a20136e · outbound

This paper cites Exploring network structure, dynamics, and function using networkx.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Exploring network structure, dynamics, and function using networkx

Reference 22

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raw_fallback, observed 2026-08-05T18:41:31.229132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.084936Z digest=sha256:3013f30a3e2e22de654bc25f07c9e45703bc54279098c120867bae50ba5e744f

Observation 8c3d9452-e8d4-44a4-b99e-04016d02f01f · outbound

This paper cites L., Ying, R., and Leskovec, J.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks L., Ying, R., and Leskovec, J

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:31.069487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.158408Z digest=sha256:00739f201f7292f4942ea7a0d21e6668061c49919448314c14e47a5f8aa58689

Observation 4f14e84d-4727-4ff2-8818-371c395cb739 · outbound

This paper cites K., Vandergheynst, P., and Gribonval, R.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks K., Vandergheynst, P., and Gribonval, R

Reference 24

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raw_fallback, observed 2026-08-05T18:41:30.902648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.223725Z digest=sha256:93e4020d1b3b7adff909e5ff13660ae9deaf6bbb10e144a106bb91d2f38ba7ed

Observation d17f3d1c-c098-4a89-8f06-f7db69f3b11a · outbound

This paper cites H., and Friedman, J.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks H., and Friedman, J

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:30.715641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.293573Z digest=sha256:124289f573cbe8a59adfe7df06e1256207cfcee4bfd53d2ecaaef4ea278175d7

Observation a57af952-617a-46dd-87e8-f31f28558c09 · outbound

This paper cites an unresolved cited work.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Unresolved cited work

Reference 26

Resolution
unresolved
raw_fallback, observed 2026-08-05T18:41:30.520034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.367791Z digest=sha256:f0a1a3c17162a54f99efa7be9a6d508d8380f47c874ad43694864a3ab1cd3971

Observation edfeec16-751e-45ed-9b98-f5fb2230a08f · outbound

This paper cites M., and Zhang, T.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks M., and Zhang, T

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:30.368430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.461093Z digest=sha256:44dd6e7068b611039c6ad06375d1cb3150de98a53a688f4fd2fc76a344dd044e

Observation 8940592f-9460-454e-ae3a-ce02a1ae50fa · outbound

This paper cites Adaptive Sampling Towards Fast Graph Representation Learning.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Adaptive Sampling Towards Fast Graph Representation Learning

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:41:22.753927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.527494Z digest=sha256:0a5b578409b31cf696599002d2296b0695e8c0cff19a0fde0f83cff7e6563550

Observation adedd71d-8d4a-4fc1-af59-3aca0247fecb · outbound

This paper cites A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares).

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)

Reference 29

Resolution
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local_arxiv, observed 2026-08-05T18:41:22.593576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.589351Z digest=sha256:efe2549ca34923b97a973b613c106e9c5852c8dedd31728c5b68c8008710e72b

Observation 503d9771-b9ff-43f0-a68b-f4195f6b1a76 · outbound

This paper cites M., Kidambi, R., Netrapalli, P., and Sidford, A.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks M., Kidambi, R., Netrapalli, P., and Sidford, A

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:30.206548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.657968Z digest=sha256:bba60b56dbcf219537dd6233657f60574a2e888c33e6641798cd90f418aed0f5

Observation ac87c034-0f43-4ffa-8926-91fd71a43115 · outbound

This paper cites Theory of graph neural networks: Representation and learning.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Theory of graph neural networks: Representation and learning

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:30.010846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.742469Z digest=sha256:1b3896e3f5d6c9356e01f7d27aa624fb14e5eb0b3c49d8fa0546ab5f040fab1c

Observation bb87baf8-05de-4594-8cf8-520e123c44e8 · outbound

This paper cites an unresolved cited work.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Unresolved cited work

Reference 32

Resolution
unresolved
raw_fallback, observed 2026-08-05T18:41:29.824867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.857900Z digest=sha256:76d1bc450aac36e39fd4f49cdbb6c299d596ec858cbc1f86e39856c486606eb2

Observation 857e8ffe-e44e-46da-924b-44d7aed32880 · outbound

This paper cites The optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks The optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:29.622755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:17.959454Z digest=sha256:bd16adffc730008a8009820109198d3dacae50a8ac871a153360fdc2495318e6

Observation 69784b83-f544-4ab7-adff-ee2cdecf9ce5 · outbound

This paper cites and Szepesvari, C.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Szepesvari, C

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:29.428686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:18.044112Z digest=sha256:a033375cfe6d9f824ed4eafc10309d239ce6f24c097e5e1cca2d498a823d9b9c

Observation a3e4ed05-3cfe-46b8-a0a3-db283bfec3cd · outbound

This paper cites and Weisfeiler, B.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Weisfeiler, B

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:29.261948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:18.129833Z digest=sha256:6fb5029865fad537347acc012ed1618087b4278a74b91ad16f3815b0ddcbef3a

Observation ed4de45c-0723-47b6-9aca-89243fb68507 · outbound

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

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Deeper insights into graph convolutional networks for semi-supervised learning

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:18.200964Z digest=sha256:5e104ba5c7f47e2331bc56ee23a30703ed44ecb6e37d10e61bd3ebb1877c9cab

Observation 94f5241d-fc8e-43d5-80a8-438de80f4c08 · outbound

This paper cites Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T18:41:18.284884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:18.284884Z digest=sha256:f0d86eb497fadd0f68fcd30ecf9ead411751de672909da85498567e03783eb6a

Observation 4f85d4ec-24c3-45a8-aeec-871273e32d08 · outbound

This paper cites A \ pac \ -bayesian approach to generalization bounds for graph neural networks.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks A \ pac \ -bayesian approach to generalization bounds for graph neural networks

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:29.036900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:18.396213Z digest=sha256:b6609c2faf638cd67004b69ffbdd1956edee45c452ea6a9689bda68f76b64b33

Observation 1f900806-9668-417d-8dfb-33350f12b598 · outbound

This paper cites Visual relationship detection with language priors, 2016.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Visual relationship detection with language priors, 2016

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:28.884647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:18.473710Z digest=sha256:bb10ca7c0fab86c78a444e8295bf440cf919c02bd4f967861521ea41e108fbca

Observation e863b2b0-6751-4766-9df7-66d05d5e0930 · outbound

This paper cites Generalization bounds for graph convolutional neural networks via rademacher complexity, 2021.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Generalization bounds for graph convolutional neural networks via rademacher complexity, 2021

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:28.697034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:18.620071Z digest=sha256:74c1d845ab6d8e160ae1ff406247a51d7aab0f2e4a276921c2268da14e3000fd

Observation af851030-7830-4616-a97e-535d040e0913 · outbound

This paper cites Subgroup Generalization and Fairness of Graph Neural Networks.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Subgroup Generalization and Fairness of Graph Neural Networks

Reference 41

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:41:22.380704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:18.717356Z digest=sha256:6478f148b76200ccf0d5f30b340a9e03525d90a8fd079414fe9aadfff6d79d71

Observation bbbbf1b5-ad54-4d81-8e08-b3454faff887 · outbound

This paper cites and Suzuki, T.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Suzuki, T

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:28.491284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:18.811017Z digest=sha256:f6a9a98a387709a354289bd82146d2507473a152874154de054ff4d3b4c69bea

Observation e5653923-6cad-49e6-b744-aa156966ba53 · outbound

This paper cites Implicit regularization or implicit conditioning? exact risk trajectories of sgd in high dimensions.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Implicit regularization or implicit conditioning? exact risk trajectories of sgd in high dimensions

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:28.339596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:18.913132Z digest=sha256:8c3983a052eaef5a32140281ad50e548ecec5a8a3a325fe0c7713cadac787526

Observation cb11845d-db09-4426-93d0-4461a75b07aa · outbound

This paper cites and Barab \'a si, A.-L.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Barab \'a si, A.-L

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:28.180450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:18.995987Z digest=sha256:e65ffabb3bd91d8b52738109a92bb5c8f041433da7ca67583b79ca9ea6cf685c

Observation d3059129-63b7-48eb-8e69-cd77f6749606 · outbound

This paper cites C., and Bonvin, A.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks C., and Bonvin, A

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:28.011195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:19.100248Z digest=sha256:c7c81e6c7fdf951821a340bd88a7e6382303459b84b09a3b8b9726b51b9c01e4

Observation 1176c815-9369-41d7-901c-7515f1f0d8ee · outbound

This paper cites Graph neural networks for materials science and chemistry.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Graph neural networks for materials science and chemistry

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:27.845898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:19.239911Z digest=sha256:73e3764668278b388dc29a30bafa072259aefc64994d195f81b4a64855e08a08

Observation 79923084-59cb-4ce2-85f7-723ec4572bd9 · outbound

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

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks A Survey on Oversmoothing in Graph Neural Networks

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-05T18:41:19.310455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:19.310455Z digest=sha256:465bc0d3c7f77c095eaaff68d04e4078b3de4dc6340bc54450c72201dbe3689e

Observation aa344fdd-ff60-4d8d-bd08-31daf1898e43 · outbound

This paper cites A Survey on The Expressive Power of Graph Neural Networks.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks A Survey on The Expressive Power of Graph Neural Networks

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-05T18:41:19.411539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:19.411539Z digest=sha256:baa8f254def5061838f51ab94e40cf7f1ce97e108eaaf2dead310a35feec9b3c

Observation 81e9ad3e-7541-4592-8557-6237a8ffe466 · outbound

This paper cites C., and Hagenbuchner, M.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks C., and Hagenbuchner, M

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:27.667924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:19.495889Z digest=sha256:f6d598342acda421863e21b781e0e9b26406b554fb2f8fc79312504bec348a06

Observation 73c2989b-6f42-4492-bfd1-a86081a574f5 · outbound

This paper cites Mspipe: Efficient temporal gnn training via staleness-aware pipeline.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Mspipe: Efficient temporal gnn training via staleness-aware pipeline

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:27.455711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:19.578816Z digest=sha256:1a7142928c33959023a07314b0716908c971ec60d9ebe46923bc3f96ea78ba7f

Observation 40b744e5-059b-4f35-b8a2-0d28227c42dd · outbound

This paper cites Spectral graph theory.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Spectral graph theory

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:27.214384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:19.676845Z digest=sha256:29df30d4a6ea1e493fdb39ec373349827fba19d440315fc2dc37bd6ce6ee99e9

Observation 15afde08-6813-4aea-8047-ea84afb57402 · outbound

This paper cites and Wu, C.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Wu, C

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:27.002575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:19.758964Z digest=sha256:2008364006333dcd87b6e62faff19f648face6c3d7d5aa2c6c1c9503199f8e2b

Observation 92e6498c-eba6-4686-967f-a02e7d57c9c2 · outbound

This paper cites PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-05T18:41:19.900502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:19.900502Z digest=sha256:67a88932b48a3587de371f5036df063ee9ffba34cac2ef396f8047ef867866de

Observation 447ca4f4-9e87-4ebe-926b-edb5ce7a69cf · outbound

This paper cites and Liu, Y.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Liu, Y

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:26.799822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:19.995077Z digest=sha256:a36f3d5950a74a92325dcb6da7dd215070c5c61a46617043ca925a5afba47318

Observation 15a16c80-36db-4337-b282-1849e5e9dc2d · outbound

This paper cites Understanding over-squashing and bottlenecks on graphs via curvature.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Understanding over-squashing and bottlenecks on graphs via curvature

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-05T18:41:20.063418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:20.063418Z digest=sha256:c297bef6ff6d45f76e6c7fdb60a73323d443b479677372d3e6f75506985be6ee

Observation 6dbbba96-4dc5-4743-9b96-4e90ccab7aa7 · outbound

This paper cites Benign overfitting in ridge regression.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Benign overfitting in ridge regression

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-05T18:41:20.145764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:20.145764Z digest=sha256:2f91dbd29efd287a0ee33ca5972315876f3669ce0d5ba27d0c5d367c95ad7b3f

Observation a7610be1-fbc2-49f1-9603-fea6af7ee04f · outbound

This paper cites and Bartlett, P.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Bartlett, P

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:26.604785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:20.226983Z digest=sha256:9615c2996833668ed4323a0dc371923f8e8b789cdc08ec4a61cb1a1774c0976b

Observation d02a315b-dc1c-438d-8ede-a45a7a3e576d · outbound

This paper cites Compound--protein interaction prediction with end-to-end learning of neural networks for graphs and sequences.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Compound--protein interaction prediction with end-to-end learning of neural networks for graphs and sequences

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:26.442209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:20.316583Z digest=sha256:1aac42f7793fc2b7f55074324b061547aadde414a6c52b63a2423c812ffb831e

Observation 37c9a8ed-8939-4207-aaea-b32a22589ce8 · outbound

This paper cites Graph spectra for complex networks.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Graph spectra for complex networks

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:26.278503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:20.380414Z digest=sha256:3b4093a99ad3212e95f15a77732ba7251c68c3b1135ede86d98f548614232416

Observation 1fddeea9-231b-420b-bf10-74086982b9a1 · outbound

This paper cites Graph Attention Networks.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Graph Attention Networks

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-05T18:41:20.447935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:20.447935Z digest=sha256:ba512b8847b3b4b6722bd145b2772e09f28fd43cdbbc19b24d953095b9b61397

Observation 9afd97e2-5703-4776-9e88-5a16b68876f4 · outbound

This paper cites and Zhang, Z.-L.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Zhang, Z.-L

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:26.044875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:20.521066Z digest=sha256:40ab9f7fb5173efd77b4e942fdf69126898283507e099e908287597063e5c3dc

Observation 574cb27d-ad19-4355-8d65-5aa1aed76c5e · outbound

This paper cites and Xu, J.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Xu, J

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:25.807860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:20.580948Z digest=sha256:ca241071807d2c74f7859e527c7add5e4474ef39ef0e9d730cd10e739ef47513

Observation 14d1500d-72a0-43a5-88d3-40837ab7c8b4 · outbound

This paper cites Simplifying graph convolutional networks.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Simplifying graph convolutional networks

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:25.611905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:20.648971Z digest=sha256:b07dc91504df5ce2225af277ef519194b54ae52ae32147590570807e11eb86e9

Observation 359db9f4-25c9-4a73-b50a-4e96c82b6b06 · outbound

This paper cites an unresolved cited work.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Unresolved cited work

Reference 64

Resolution
unresolved
raw_fallback, observed 2026-08-05T18:41:25.384887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:20.709432Z digest=sha256:dce992fa9a34e0640d8570902d17cc2e193c92ea1da28b917fdda2cfcfc0e113

Observation 07ad373d-0b6e-4033-b6f7-94421808bd90 · outbound

This paper cites Handling distribution shifts on graphs: An invariance perspective, 2022.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Handling distribution shifts on graphs: An invariance perspective, 2022

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:25.193804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:20.767662Z digest=sha256:ea939745e2d02d6305dba5262f0656ed7e9f0ee7a3f3b0e86f8e706f6bd7c263

Observation 4d48f0fb-43eb-4288-94ea-70446bbb1f6c · outbound

This paper cites B., and Fei-Fei, L.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks B., and Fei-Fei, L

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:25.035167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:20.860253Z digest=sha256:ee3ee0f7f3eb6a12572debc174aabb94198837a5cacd4ee5864aed6611f7cc58

Observation 4cafe744-3882-4d6c-bff3-3580771ef422 · outbound

This paper cites and Hsu, D.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks and Hsu, D

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:24.860976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:20.923500Z digest=sha256:3c480a5c1daeb0b45d4c17e9b9d9e008bc639da584e4fba146ed55a662d70de1

Observation ce8ec512-70ca-4672-a803-6adc64f95142 · outbound

This paper cites How Powerful are Graph Neural Networks?.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks How Powerful are Graph Neural Networks?

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-05T18:41:21.010034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:21.010034Z digest=sha256:3fdf6e91685a007c69a399ab1b06844e5d42a3aeff9b36b39c02cfefe5161ff7

Observation 1f365408-59f6-4550-9ca3-0670c1b32de1 · outbound

This paper cites Z., Guo, Z., Zhou, K., Zhang, W., and Liu, Z.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Z., Guo, Z., Zhou, K., Zhang, W., and Liu, Z

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:24.653730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:21.098054Z digest=sha256:13e92d2576b974f209b440304a9c405f4cb5265a13cb98df6fd7e91c1b49ba61

Observation edbf1814-2eb2-4ebd-85da-cea663c105d4 · outbound

This paper cites Neural motifs: Scene graph parsing with global context, 2018.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Neural motifs: Scene graph parsing with global context, 2018

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:24.423340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:21.189003Z digest=sha256:0a209adab9c9cc216d86c4ef06c904338f5d809e38dc27c0beff3c651a1cd15b

Observation a1efca9d-d4a9-4483-a920-fc8ab25bc5b3 · outbound

This paper cites The Expressive Power of Graph Neural Networks: A Survey.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks The Expressive Power of Graph Neural Networks: A Survey

Reference 71

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:41:22.035084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:21.244827Z digest=sha256:93b91a38d5e3b26b91af3f9e718a4055b8188900c09aa57341e7ce7d8e3d9ef0

Observation 0d3f6650-c556-4e11-a6a8-2fa24eb828b8 · outbound

This paper cites A complete expressiveness hierarchy for subgraph gnns via subgraph weisfeiler-lehman tests.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks A complete expressiveness hierarchy for subgraph gnns via subgraph weisfeiler-lehman tests

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:24.030389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:21.301239Z digest=sha256:f2523b51d5f960ced5e49ea1404b57480d159307b43c34da834ff6880a95c74b

Observation 8ff7cf5f-aa9a-4e6c-ba7f-281e8c901ca1 · outbound

This paper cites Rethinking the Expressive Power of GNNs via Graph Biconnectivity.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Rethinking the Expressive Power of GNNs via Graph Biconnectivity

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-05T18:41:21.350824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:21.350824Z digest=sha256:f6f43d4329e9cc0fc00cea3e87bdb4cfc83d9aa32af5f0519a9a063843a75dd3

Observation ce8ac558-f3bc-41c9-8744-2f61a21985a9 · outbound

This paper cites an unresolved cited work.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Unresolved cited work

Reference 74

Resolution
unresolved
raw_fallback, observed 2026-08-05T18:41:23.717698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:21.440420Z digest=sha256:64cab2586a4b602dd2a1829cc4e80f7b1073d587d74c9ae4ac40b62a7f328afe

Observation 50d3a005-442f-46ca-9185-a9380f7bcbbe · outbound

This paper cites Shift-robust gnns: Overcoming the limitations of localized graph training data, 2021.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Shift-robust gnns: Overcoming the limitations of localized graph training data, 2021

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:23.481731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:21.507764Z digest=sha256:48a24575a18c5ef185e3682fc30ba906f30aeafac0f466c2af76e23328676ebd

Observation fe244dba-ab6b-4cb5-ad63-bad77c4b1cb6 · outbound

This paper cites P., and Kakade, S.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks P., and Kakade, S

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:23.308716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:21.593164Z digest=sha256:555e3670e3ae0534a3ffc448f9751523015f639b309994a3d8c60e12b9536a8b

Observation 620fbac1-10b8-4818-a207-e92f3d3dbd27 · outbound

This paper cites Benign overfitting of constant-stepsize sgd for linear regression.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Benign overfitting of constant-stepsize sgd for linear regression

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:41:23.145889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:21.689195Z digest=sha256:353f0243c22e00f17d93e141a454d911146bcffa1f36fc7f5128f46c77e7b63b

Observation b3290784-6d94-4c91-914c-0051c6cf463f · outbound

This paper cites an unresolved cited work.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks Unresolved cited work

Reference 78

Resolution
unresolved
raw_fallback, observed 2026-08-05T18:41:22.960965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:41:21.777430Z digest=sha256:1e1895b75161c6547eaa7be8e53fdaded2f6e1e4019ca602d5b69fbe7db41d78

Observation 3acbd04a-3c27-46f1-951f-cd0f2aadb6d5 · outbound

This paper cites write newline.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks write newline

Reference 79

Resolution
unresolved
no resolver link, observed 2026-08-05T18:41:21.823852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:41:21.823852Z digest=sha256:02915d100f8696a9b997b8a90ef1d124f0e15cab3c9cba13eed3244d3e7f3241

Pith citing papers

No inbound Pith citation observations are available.