Memorization in Graph Neural Networks | Paper Citation Record

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This paper cites MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing.

Memorization in Graph Neural Networks MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing

Reference 1

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This paper cites On the bottleneck of graph neural networks and its practical implications.

Memorization in Graph Neural Networks On the bottleneck of graph neural networks and its practical implications

Reference 2

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This paper cites Generalization Error of Graph Neural Networks in the Mean-field Regime.

Memorization in Graph Neural Networks Generalization Error of Graph Neural Networks in the Mean-field Regime

Reference 3

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This paper cites Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks.

Memorization in Graph Neural Networks Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks

Reference 4

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This paper cites A closer look at memorization in deep networks.

Memorization in Graph Neural Networks A closer look at memorization in deep networks

Reference 5

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This paper cites Deep learning through the lens of example difficulty.

Memorization in Graph Neural Networks Deep learning through the lens of example difficulty

Reference 6

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This paper cites Emergence of scaling in random networks.

Memorization in Graph Neural Networks Emergence of scaling in random networks

Reference 7

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This paper cites The Pitfalls of Memorization: When Memorization Hurts Generalization.

Memorization in Graph Neural Networks The Pitfalls of Memorization: When Memorization Hurts Generalization

Reference 8

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This paper cites Graph neural networks use graphs when they shouldn’t, 2024.

Memorization in Graph Neural Networks Graph neural networks use graphs when they shouldn’t, 2024

Reference 9

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Observation e4083dd0-715c-4c03-9a6b-e831673574e4 · outbound

This paper cites Make heterophily graphs better fit gnn: A graph rewiring approach, 2022.

Memorization in Graph Neural Networks Make heterophily graphs better fit gnn: A graph rewiring approach, 2022

Reference 10

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Observation cba4570e-78ac-4e08-800f-8086d5e5568b · outbound

This paper cites Memorization and optimization in deep neural networks with minimum over-parameterization.

Memorization in Graph Neural Networks Memorization and optimization in deep neural networks with minimum over-parameterization

Reference 11

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Observation e8bd7345-1136-4852-b9df-f9328d9a75ac · outbound

This paper cites Biognn: How graph neural networks can solve biological problems.

Memorization in Graph Neural Networks Biognn: How graph neural networks can solve biological problems

Reference 12

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Observation 28af3de1-8988-4ec1-9b19-e99d7f53bf05 · outbound

This paper cites How attentive are graph attention networks? In International Conference on Learning Representations, 2022.

Memorization in Graph Neural Networks How attentive are graph attention networks? In International Conference on Learning Representations, 2022

Reference 13

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Observation 69e6de8f-f90d-48ef-9c7a-4004e04674e8 · outbound

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

Memorization in Graph Neural Networks Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 14

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This paper cites Extracting training data from large language models.

Memorization in Graph Neural Networks Extracting training data from large language models

Reference 15

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This paper cites Membership inference attacks from first principles.

Memorization in Graph Neural Networks Membership inference attacks from first principles

Reference 16

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This paper cites The privacy onion effect: Memorization is relative.

Memorization in Graph Neural Networks The privacy onion effect: Memorization is relative

Reference 17

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Observation 7bc7ee87-4ba5-429e-9e47-35418b5f713b · outbound

This paper cites Quantifying Memorization Across Neural Language Models.

Memorization in Graph Neural Networks Quantifying Memorization Across Neural Language Models

Reference 18

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This paper cites On kernel-target alignment.

Memorization in Graph Neural Networks On kernel-target alignment

Reference 19

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Observation 6d44fffb-5237-46cf-9926-2d08c371ddd3 · outbound

This paper cites Attentive walk-aggregating graph neural networks.

Memorization in Graph Neural Networks Attentive walk-aggregating graph neural networks

Reference 20

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Observation f26413f2-2011-4a1f-a6e0-c509ced76552 · outbound

This paper cites Does learning require memorization? a short tale about a long tail.

Memorization in Graph Neural Networks Does learning require memorization? a short tale about a long tail

Reference 21

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This paper cites How powerful are k-hop message passing graph neural networks.

Memorization in Graph Neural Networks How powerful are k-hop message passing graph neural networks

Reference 22

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Memorization in Graph Neural Networks Unresolved cited work

Reference 23

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This paper cites Schoenholz, Patrick F.

Memorization in Graph Neural Networks Schoenholz, Patrick F

Reference 24

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This paper cites Giraldo, Konstantinos Skianis, Thierry Bouwmans, and Fragkiskos D.

Memorization in Graph Neural Networks Giraldo, Konstantinos Skianis, Thierry Bouwmans, and Fragkiskos D

Reference 25

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This paper cites Understanding the difficulty of training deep feedforward neural networks.

Memorization in Graph Neural Networks Understanding the difficulty of training deep feedforward neural networks

Reference 26

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Memorization in Graph Neural Networks Unresolved cited work

Reference 27

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This paper cites Hamilton, Rex Ying, and Jure Leskovec.

Memorization in Graph Neural Networks Hamilton, Rex Ying, and Jure Leskovec

Reference 28

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This paper cites Hamilton, Zhitao Ying, and Jure Leskovec.

Memorization in Graph Neural Networks Hamilton, Zhitao Ying, and Jure Leskovec

Reference 29

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Observation 61baf7f2-f2ab-4736-b375-469c944253b7 · outbound

This paper cites Open Graph Benchmark: Datasets for Machine Learning on Graphs.

Memorization in Graph Neural Networks Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 30

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This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

Memorization in Graph Neural Networks Neural tangent kernel: Convergence and generalization in neural networks

Reference 31

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This paper cites Spectral graph pruning against over-squashing and over-smoothing.

Memorization in Graph Neural Networks Spectral graph pruning against over-squashing and over-smoothing

Reference 32

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This paper cites Banerjee, and Guido Montufar.

Memorization in Graph Neural Networks Banerjee, and Guido Montufar

Reference 33

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This paper cites Homophily influences ranking of minorities in social networks.

Memorization in Graph Neural Networks Homophily influences ranking of minorities in social networks

Reference 34

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Observation 0df95029-86fb-4698-bf9d-c9c900188647 · outbound

This paper cites Not too little, not too much: a theoretical analysis of graph (over)smoothing.

Memorization in Graph Neural Networks Not too little, not too much: a theoretical analysis of graph (over)smoothing

Reference 35

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This paper cites Convergence and Stability of Graph Convolutional Networks on Large Random Graphs.

Memorization in Graph Neural Networks Convergence and Stability of Graph Convolutional Networks on Large Random Graphs

Reference 36

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This paper cites Kipf and Max Welling.

Memorization in Graph Neural Networks Kipf and Max Welling

Reference 37

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Observation 5cc8453d-7084-4837-ba77-3d783ddf0fe3 · outbound

This paper cites Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond.

Memorization in Graph Neural Networks Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond

Reference 38

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Observation f1b371d0-c53f-40a9-b021-a2e916881856 · outbound

This paper cites Towards bridging generalization and expressivity of graph neural networks, 2024.

Memorization in Graph Neural Networks Towards bridging generalization and expressivity of graph neural networks, 2024

Reference 39

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This paper cites Can Neural Network Memorization Be Localized?.

Memorization in Graph Neural Networks Can Neural Network Memorization Be Localized?

Reference 40

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This paper cites Automat- ing the construction of internet portals with machine learning.

Memorization in Graph Neural Networks Automat- ing the construction of internet portals with machine learning

Reference 41

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This paper cites Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk.

Memorization in Graph Neural Networks Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk

Reference 42

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This paper cites TUDataset: A collection of benchmark datasets for learning with graphs.

Memorization in Graph Neural Networks TUDataset: A collection of benchmark datasets for learning with graphs

Reference 43

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This paper cites Are GATs out of balance? In Thirty-seventh Conference on Neural Information Processing Systems, 2023.

Memorization in Graph Neural Networks Are GATs out of balance? In Thirty-seventh Conference on Neural Information Processing Systems, 2023

Reference 44

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Observation ffded4b2-efaa-4f3c-ba91-24d0e48e991a · outbound

This paper cites Query-driven active surveying for collective classification.

Memorization in Graph Neural Networks Query-driven active surveying for collective classification

Reference 45

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Observation 3e0c7961-428e-44ca-90c4-df6c8e951015 · outbound

This paper cites Revisiting over-smoothing and over-squashing using ollivier-ricci curvature, 2023.

Memorization in Graph Neural Networks Revisiting over-smoothing and over-squashing using ollivier-ricci curvature, 2023

Reference 46

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This paper cites k-hop graph neural networks,.

Memorization in Graph Neural Networks k-hop graph neural networks,

Reference 47

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This paper cites Geom-gcn: Geo- metric graph convolutional networks.

Memorization in Graph Neural Networks Geom-gcn: Geo- metric graph convolutional networks

Reference 48

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This paper cites Charac- terizing graph datasets for node classification: Homophily-heterophily dichotomy and be- yond.

Memorization in Graph Neural Networks Charac- terizing graph datasets for node classification: Homophily-heterophily dichotomy and be- yond

Reference 49

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This paper cites A critical look at evaluation of gnns under heterophily: Are we really making progress? In The Eleventh International Conference on Learning Representations, 2023.

Memorization in Graph Neural Networks A critical look at evaluation of gnns under heterophily: Are we really making progress? In The Eleventh International Conference on Learning Representations, 2023

Reference 50

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This paper cites Recipe for a General, Powerful, Scalable Graph Transformer.

Memorization in Graph Neural Networks Recipe for a General, Powerful, Scalable Graph Transformer

Reference 51

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

Memorization in Graph Neural Networks Graph neural networks for materials science and chemistry

Reference 52

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Memorization in Graph Neural Networks Unresolved cited work

Reference 53

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Observation 6fd88c90-f208-412f-bd6e-004f8715756a · outbound

This paper cites GNNs getting comfy: Com- munity and feature similarity guided rewiring.

Memorization in Graph Neural Networks GNNs getting comfy: Com- munity and feature similarity guided rewiring

Reference 54

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Observation 31ad7bd0-f81e-43dd-a65f-92f558cff1d1 · outbound

This paper cites The graph neural network model.

Memorization in Graph Neural Networks The graph neural network model

Reference 55

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This paper cites Collective classification in network data.

Memorization in Graph Neural Networks Collective classification in network data

Reference 56

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This paper cites Pitfalls of graph neural network evaluation, 2019.

Memorization in Graph Neural Networks Pitfalls of graph neural network evaluation, 2019

Reference 57

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Observation 1b8f7a16-6c1a-4ff8-817c-93f83dbdd0a9 · outbound

This paper cites Multi-scale Attributed Node Embedding.

Memorization in Graph Neural Networks Multi-scale Attributed Node Embedding

Reference 58

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Observation 0e5b8678-65a7-492e-9fb8-74ad0f2c2f1c · outbound

This paper cites Membership inference attacks against machine learning models.

Memorization in Graph Neural Networks Membership inference attacks against machine learning models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T15:53:57.272780Z

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Observation 4ad4f228-bd30-4970-aa6f-406ff446aa63 · outbound

This paper cites Towards understanding generalization of graph neural networks.

Memorization in Graph Neural Networks Towards understanding generalization of graph neural networks

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:54:06.096498Z

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Observation 51850cc4-92e8-4db4-8cc0-04afdc54bee5 · outbound

This paper cites Bronstein.

Memorization in Graph Neural Networks Bronstein

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:54:05.608901Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2b2cea8c-f514-4242-a8ae-c2b759b8ce1b · outbound

This paper cites Graph Attention Networks.

Memorization in Graph Neural Networks Graph Attention Networks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:54:05.199623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8b903f9f-d5b4-4ca1-b1b1-224c17a21c4a · outbound

This paper cites Graph neural networks in particle physics.

Memorization in Graph Neural Networks Graph neural networks in particle physics

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-05T15:53:57.140213Z

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Observation a1a0e9b9-8abf-43b8-be62-120bb45be956 · outbound

This paper cites A Manifold Perspective on the Statistical Generalization of Graph Neural Networks.

Memorization in Graph Neural Networks A Manifold Perspective on the Statistical Generalization of Graph Neural Networks

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:53:59.900314Z

Source-reported events for the cited work

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Observation 5647f6ca-0b5c-454a-a883-50296a25b3a6 · outbound

This paper cites Simplifying graph convolutional networks.

Memorization in Graph Neural Networks Simplifying graph convolutional networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:54:04.483379Z

Source-reported events for the cited work

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Observation 2e83f67b-8f99-40c9-8204-6ad84e385750 · outbound

This paper cites How graph neural networks learn: Lessons from training dynamics in function space, 2024.

Memorization in Graph Neural Networks How graph neural networks learn: Lessons from training dynamics in function space, 2024

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:54:04.168047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 79ba0b58-b477-4ff5-968b-7fb503f086e9 · outbound

This paper cites Understanding deep learning requires rethinking generalization.

Memorization in Graph Neural Networks Understanding deep learning requires rethinking generalization

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:54:03.820739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a08c7bd9-0559-4894-89b5-d44820b377f8 · outbound

This paper cites Memorization in self-supervised learning improves downstream generalization.

Memorization in Graph Neural Networks Memorization in self-supervised learning improves downstream generalization

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:54:04.842169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f42514a7-9365-41eb-86e0-322a8f0cf943 · outbound

This paper cites Be- yond homophily in graph neural networks: Current limitations and effective designs.

Memorization in Graph Neural Networks Be- yond homophily in graph neural networks: Current limitations and effective designs

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:54:03.493496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e577da71-9499-4ab5-adc5-f0021cdf1a1b · outbound

This paper cites an unresolved cited work.

Memorization in Graph Neural Networks Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:54:03.138915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c02dcdf5-beab-4cd5-bbc8-6324c46e7b02 · outbound

This paper cites an unresolved cited work.

Memorization in Graph Neural Networks Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:54:02.760978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bf839a4a-4644-4b89-b4bc-f4399689689c · outbound

This paper cites Inequality 10 states that γ must be greater than or equal to this large angle (δ − ϵ).

Memorization in Graph Neural Networks Inequality 10 states that γ must be greater than or equal to this large angle (δ − ϵ)

Reference 76

Resolution: malformed identifier; raw_fallback, observed 2026-08-05T15:54:02.387902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3375b53a-6a4c-4c6e-88c0-933df55386f4 · outbound

This paper cites Our theoretical results in Section 4 demonstrate that one of the key factors influencing memorization in GNNs is the graph homophily.

Memorization in Graph Neural Networks Our theoretical results in Section 4 demonstrate that one of the key factors influencing memorization in GNNs is the graph homophily

Reference 77

Resolution: malformed identifier; raw_fallback, observed 2026-08-05T15:54:01.925640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5f08ca9b-ceed-4c83-befe-26773f38c018 · outbound

This paper cites an unresolved cited work.

Memorization in Graph Neural Networks Unresolved cited work

Reference 2001

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:54:15.853047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d9470b05-5a76-4c75-b610-b59cde8f8470 · outbound

This paper cites URL https://proceedings.mlr.press/v9/glorot10a.html.

Memorization in Graph Neural Networks URL https://proceedings.mlr.press/v9/glorot10a.html

Reference 2010

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:54:13.578059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 47f9c5ff-09b4-4383-83f2-708f6401f4d8 · outbound

This paper cites doi: 10.1038/s41598-018-29405-7.

Memorization in Graph Neural Networks doi: 10.1038/s41598-018-29405-7

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-05T15:53:53.230001Z

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Observation 44bb15bb-e1f3-41ca-8ff6-72f2b5db5ee6 · outbound

This paper cites k-hop Graph Neural Networks.

Memorization in Graph Neural Networks k-hop Graph Neural Networks

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:54:00.366113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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