On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks | Paper Citation Record

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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

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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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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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This paper cites L., Long, P.

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

Reference 4

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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

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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

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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

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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

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

Reference 9

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This paper cites and Bach, F.

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

Reference 10

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This paper cites S., Foster, D.

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

Reference 11

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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

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This paper cites and Wager, S.

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

Reference 13

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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

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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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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

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This paper cites real-world.

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

Reference 17

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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

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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

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This paper cites S., Riley, P.

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

Reference 20

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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

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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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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

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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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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

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

Reference 26

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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

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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

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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

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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

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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

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

Reference 32

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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

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

Reference 34

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This paper cites and Weisfeiler, B.

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

Reference 35

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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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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

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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

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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

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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

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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

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

Reference 42

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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

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

Reference 44

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

Reference 45

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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

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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

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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

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

Reference 49

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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

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This paper cites Spectral graph theory.

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

Reference 51

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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

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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

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This paper cites and Liu, Y.

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

Reference 54

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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

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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

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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

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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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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 2026-08-19T06:32:44.657259+00:00.

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.

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Outbound references

Pith citing papers