Paper Citation Record · LEDGER

Training a Label-Noise-Resistant GNN with Reduced Complexity

As of 18 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2411.11020.

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

pith.paper-citation-record.v1
2411.11020 v1

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measured 48 of 48 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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48 of 48 outbound references displayed

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Outbound references

Observation f8523b30-11fd-4a6b-beb3-e084fae2e357 · outbound

This paper cites Adversarial label-flipping attack and defense for graph neural net- works,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Adversarial label-flipping attack and defense for graph neural net- works,

Reference 1

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Observation f8a1a77a-4d18-4c3c-ac45-41cb9cc3c42e · outbound

This paper cites Inductive rep- resentation learning on large graphs,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Inductive rep- resentation learning on large graphs,

Reference 2

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Observation fcba64ff-8579-427a-bd89-1b60edcd1cd8 · outbound

This paper cites Modeling network- level traffic flow transitions on sparse data,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Modeling network- level traffic flow transitions on sparse data,

Reference 3

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Observation 29826df2-63dc-4c91-8ae8-172dcb0eefef · outbound

This paper cites Stochastic weight completion for road networks using graph con- volutional networks,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Stochastic weight completion for road networks using graph con- volutional networks,

Reference 4

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Observation 694b5f65-b936-42fa-a713-99a0acf7c284 · outbound

This paper cites An edge feature aware heterogeneous graph neural net- work model to support tax evasion detection,.

Training a Label-Noise-Resistant GNN with Reduced Complexity An edge feature aware heterogeneous graph neural net- work model to support tax evasion detection,

Reference 5

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Observation bb920f57-5589-4c4f-b223-2eb5a8520291 · outbound

This paper cites Tax evasion detection with fbne-pu algorithm based on pncgcn and pu learning,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Tax evasion detection with fbne-pu algorithm based on pncgcn and pu learning,

Reference 6

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Observation 49579d8b-d78c-4abf-91b2-d05fea8486fb · outbound

This paper cites Spectral Networks and Locally Connected Networks on Graphs.

Training a Label-Noise-Resistant GNN with Reduced Complexity Spectral Networks and Locally Connected Networks on Graphs

Reference 7

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This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Training a Label-Noise-Resistant GNN with Reduced Complexity Semi-Supervised Classification with Graph Convolutional Networks

Reference 8

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This paper cites How Powerful are Graph Neural Networks?.

Training a Label-Noise-Resistant GNN with Reduced Complexity How Powerful are Graph Neural Networks?

Reference 9

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This paper cites Graph based semi- supervised learning with convolution neural networks to classify crisis related tweets,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Graph based semi- supervised learning with convolution neural networks to classify crisis related tweets,

Reference 10

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Observation 004de759-8081-4130-9639-16318b14ab37 · outbound

This paper cites Grale: Designing networks for graph learning,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Grale: Designing networks for graph learning,

Reference 11

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This paper cites Data augmentation for graph neural networks,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Data augmentation for graph neural networks,

Reference 12

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Observation c7f8e43c-704e-47a1-a6e0-c868d8a8211c · outbound

This paper cites Hard sample aware network for contrastive deep graph clustering,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Hard sample aware network for contrastive deep graph clustering,

Reference 13

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This paper cites Deep graph clustering via dual correlation reduction,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Deep graph clustering via dual correlation reduction,

Reference 14

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Observation cf95fa95-11d8-45ed-8504-da78e24a2e8c · outbound

This paper cites Cldg: Contrastive learning on dynamic graphs,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Cldg: Contrastive learning on dynamic graphs,

Reference 15

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Observation 2b874ab5-a49d-432d-b676-757da23e1bdb · outbound

This paper cites Nrgnn: Learning a label noise resistant graph neural network on sparsely and noisily labeled graphs,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Nrgnn: Learning a label noise resistant graph neural network on sparsely and noisily labeled graphs,

Reference 16

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Observation ae9a52ce-4221-4b53-b9df-c7ffec7f0e2f · outbound

This paper cites Robust triple-matrix-recovery-based auto- weighted label propagation for classification,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Robust triple-matrix-recovery-based auto- weighted label propagation for classification,

Reference 17

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Observation c3b1621b-7931-431a-b1b3-8aa3ee1636df · outbound

This paper cites Robust training of graph neural networks via noise governance,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Robust training of graph neural networks via noise governance,

Reference 18

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This paper cites Gnn cleaner: Label cleaner for graph structured data,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Gnn cleaner: Label cleaner for graph structured data,

Reference 19

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Observation 7d8f9938-79f1-4f39-9b42-e8fe7d87dcad · outbound

This paper cites Learning on graphs under label noise,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Learning on graphs under label noise,

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This paper cites DivideMix: Learning with Noisy Labels as Semi-supervised Learning.

Training a Label-Noise-Resistant GNN with Reduced Complexity DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 21

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This paper cites Deep self-learning from noisy labels,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Deep self-learning from noisy labels,

Reference 22

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This paper cites Joint optimization framework for learning with noisy labels,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Joint optimization framework for learning with noisy labels,

Reference 23

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This paper cites Provably consistent partial- label learning,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Provably consistent partial- label learning,

Reference 24

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Training a Label-Noise-Resistant GNN with Reduced Complexity Partial label learning with batch label correction,

Reference 25

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This paper cites Making deep neural networks robust to label noise: A loss correction approach,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Making deep neural networks robust to label noise: A loss correction approach,

Reference 26

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This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Co-teaching: Robust training of deep neural networks with extremely noisy labels,

Reference 27

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This paper cites How does disagreement help generaliza- tion against label corruption?.

Training a Label-Noise-Resistant GNN with Reduced Complexity How does disagreement help generaliza- tion against label corruption?

Reference 28

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This paper cites Generalized cross entropy loss for training deep neural networks with noisy la- bels,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Generalized cross entropy loss for training deep neural networks with noisy la- bels,

Reference 29

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This paper cites Learning with Instance-Dependent Label Noise: A Sample Sieve Approach.

Training a Label-Noise-Resistant GNN with Reduced Complexity Learning with Instance-Dependent Label Noise: A Sample Sieve Approach

Reference 30

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Training a Label-Noise-Resistant GNN with Reduced Complexity Class2simi: A noise reduction per- spective on learning with noisy labels,

Reference 31

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This paper cites Instance-dependent label- noise learning with manifold-regularized transition ma- trix estimation,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Instance-dependent label- noise learning with manifold-regularized transition ma- trix estimation,

Reference 32

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Observation 145e7ba8-834c-4ace-8d6a-e8a61051dbbe · outbound

This paper cites Peer loss functions: Learning from noisy labels without knowing noise rates,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Peer loss functions: Learning from noisy labels without knowing noise rates,

Reference 33

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This paper cites Robust loss functions under label noise for deep neural networks,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Robust loss functions under label noise for deep neural networks,

Reference 34

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Training a Label-Noise-Resistant GNN with Reduced Complexity Learning from noisy labels with complementary loss functions,

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This paper cites Neural message passing for quantum chem- istry,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Neural message passing for quantum chem- istry,

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This paper cites Dual t: Reducing estimation error for transition matrix in label-noise learning,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Dual t: Reducing estimation error for transition matrix in label-noise learning,

Reference 37

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This paper cites Part-dependent label noise: Towards instance-dependent label noise,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Part-dependent label noise: Towards instance-dependent label noise,

Reference 38

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Observation 8e3db5c1-487a-416b-b488-9e5924c87110 · outbound

This paper cites Estimating instance-dependent bayes-label transition matrix using a deep neural network,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Estimating instance-dependent bayes-label transition matrix using a deep neural network,

Reference 39

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This paper cites L dmi: A novel information-theoretic loss function for training deep nets robust to label noise,.

Training a Label-Noise-Resistant GNN with Reduced Complexity L dmi: A novel information-theoretic loss function for training deep nets robust to label noise,

Reference 40

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This paper cites Classification with noisy labels by importance reweighting,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Classification with noisy labels by importance reweighting,

Reference 41

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This paper cites Birds of a feather: Homophily in social networks,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Birds of a feather: Homophily in social networks,

Reference 42

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Observation e8c59c9b-b112-4838-8b6e-8684d1628183 · outbound

This paper cites Hierarchical grammar-induced geometry for data-efficient molecular property predic- tion,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Hierarchical grammar-induced geometry for data-efficient molecular property predic- tion,

Reference 43

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Observation d8ff7973-8842-4a7c-a849-536fa053bbd5 · outbound

This paper cites Collective classification in network data,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Collective classification in network data,

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This paper cites IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research.

Training a Label-Noise-Resistant GNN with Reduced Complexity IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research

Reference 45

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Training a Label-Noise-Resistant GNN with Reduced Complexity Open graph benchmark: Datasets for machine learning on graphs,

Reference 46

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This paper cites Learning from massive noisy labeled data for image classification,.

Training a Label-Noise-Resistant GNN with Reduced Complexity Learning from massive noisy labeled data for image classification,

Reference 47

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Observation d1ced94a-69e3-4ae1-b58f-0cc26f0229dd · outbound

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

Training a Label-Noise-Resistant GNN with Reduced Complexity Deeper insights into graph convolutional networks for semi-supervised learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:07:19.248886Z

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