PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification | Paper Citation Record

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This paper cites Generalization bounds for mixing processes via delayed online-to-PAC conversions.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Generalization bounds for mixing processes via delayed online-to-PAC conversions

Reference 1

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This paper cites User-friendly introduction to PAC-Bayes bounds.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification User-friendly introduction to PAC-Bayes bounds

Reference 2

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This paper cites Simpler PAC-Bayesian bounds for hostile data.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Simpler PAC-Bayesian bounds for hostile data

Reference 3

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This paper cites Robust bounds on risk-sensitive functionals via R \' e nyi divergence.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Robust bounds on risk-sensitive functionals via R \' e nyi divergence

Reference 4

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Observation a1a4f49f-ad87-4e46-ae1b-8887e1a39fed · outbound

This paper cites PAC-Bayesian bounds based on the R \' e nyi divergence.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification PAC-Bayesian bounds based on the R \' e nyi divergence

Reference 5

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This paper cites Graph neural networks with convolutional arma filters.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Graph neural networks with convolutional arma filters

Reference 6

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This paper cites Zheng, Hongyun Cai, Kevin Chen-Chuan Chang, and Erik Cambria.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Zheng, Hongyun Cai, Kevin Chen-Chuan Chang, and Erik Cambria

Reference 7

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This paper cites Simple and deep graph convolutional networks.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Simple and deep graph convolutional networks

Reference 8

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This paper cites Adaptive universal generalized pagerank graph neural network.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Adaptive universal generalized pagerank graph neural network

Reference 9

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This paper cites A unified recipe for deriving (time-uniform) PAC-Bayes bounds.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification A unified recipe for deriving (time-uniform) PAC-Bayes bounds

Reference 10

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This paper cites On provable benefits of depth in training graph convolutional networks.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification On provable benefits of depth in training graph convolutional networks

Reference 11

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This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Convolutional neural networks on graphs with fast localized spectral filtering

Reference 12

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This paper cites Generalization error bounds via R \' e nyi , f -divergences and maximal leakage.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Generalization error bounds via R \' e nyi , f -divergences and maximal leakage

Reference 13

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This paper cites Vankadara, and Debarghya Ghoshdastidar.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Vankadara, and Debarghya Ghoshdastidar

Reference 14

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This paper cites Recent Developments in GNNs for Drug Discovery.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Recent Developments in GNNs for Drug Discovery

Reference 15

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This paper cites A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

Reference 16

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This paper cites A survey of graph neural networks for recommender systems: Challenges, methods, and directions.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification A survey of graph neural networks for recommender systems: Challenges, methods, and directions

Reference 17

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This paper cites Generalization and representational limits of graph neural networks.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Generalization and representational limits of graph neural networks

Reference 18

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This paper cites Predict then propagate: Graph neural networks meet personalized pagerank.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Predict then propagate: Graph neural networks meet personalized pagerank

Reference 19

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This paper cites Schoenholz, Patrick F.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Schoenholz, Patrick F

Reference 20

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This paper cites A new model for learning in graph domains.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification A new model for learning in graph domains

Reference 21

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This paper cites Inductive representation learning on large graphs.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Inductive representation learning on large graphs

Reference 22

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This paper cites Foundations of Deep Learning.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Foundations of Deep Learning

Reference 23

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This paper cites Bernnet: Learning arbitrary graph spectral filters via bernstein approximation.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Bernnet: Learning arbitrary graph spectral filters via bernstein approximation

Reference 24

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This paper cites Generalization bounds via information density and conditional information density.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Generalization bounds via information density and conditional information density

Reference 25

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This paper cites Generalization bounds: Perspectives from information theory and PAC-Bayes.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Generalization bounds: Perspectives from information theory and PAC-Bayes

Reference 26

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This paper cites Large deviations for sums of partly dependent random variables.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Large deviations for sums of partly dependent random variables

Reference 27

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This paper cites Kipf and Max Welling.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Kipf and Max Welling

Reference 28

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This paper cites Kipf and Max Welling.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Kipf and Max Welling

Reference 29

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This paper cites Concentration of measure without independence: A unified approach via the martingale method.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Concentration of measure without independence: A unified approach via the martingale method

Reference 30

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This paper cites Concentration inequalities for dependent random variables via the martingale method.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Concentration inequalities for dependent random variables via the martingale method

Reference 31

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This paper cites Learning theory and algorithms for forecasting non-stationary time series.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Learning theory and algorithms for forecasting non-stationary time series

Reference 32

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This paper cites Discrepancy-based theory and algorithms for forecasting non stationary time series.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Discrepancy-based theory and algorithms for forecasting non stationary time series

Reference 33

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PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Unresolved cited work

Reference 34

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This paper cites A PAC-Bayesian approach to generalization bounds for graph neural networks.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification A PAC-Bayesian approach to generalization bounds for graph neural networks

Reference 35

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This paper cites Yu, and Chuan Shi.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Yu, and Chuan Shi

Reference 36

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This paper cites Position: Graph foundation models are already here.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Position: Graph foundation models are already here

Reference 37

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This paper cites Tweedie, and Peter W.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Tweedie, and Peter W

Reference 38

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This paper cites Bronstein, Martin Grohe, and Stefanie Jegelka.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Bronstein, Martin Grohe, and Stefanie Jegelka

Reference 39

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This paper cites Optimization and generalization analysis of transduction through gradient boosting and application to multi-scale graph neural networks.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Optimization and generalization analysis of transduction through gradient boosting and application to multi-scale graph neural networks

Reference 40

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This paper cites Graph neural networks for intelligent transportation systems: A survey.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Graph neural networks for intelligent transportation systems: A survey

Reference 41

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PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Real and complex analysis

Reference 42

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PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification The graph neural network model

Reference 43

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PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Towards understanding generalization of graph neural networks

Reference 44

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Observation 1da7a8ef-34dd-486e-bb62-1ee87ab06d80 · outbound

This paper cites R \' e nyi Divergence and Kullback-Leibler Divergence.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification R \' e nyi Divergence and Kullback-Leibler Divergence

Reference 45

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PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Unresolved cited work

Reference 46

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PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Unresolved cited work

Reference 47

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Observation eae1c168-3ae2-457c-ad5b-4beeb3990cc6 · outbound

This paper cites Survey on Generalization Theory for Graph Neural Networks.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Survey on Generalization Theory for Graph Neural Networks

Reference 48

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PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Graph attention networks

Reference 49

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This paper cites Wainwright.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Wainwright

Reference 50

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This paper cites A Review on Graph Neural Network Methods in Financial Applications.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification A Review on Graph Neural Network Methods in Financial Applications

Reference 51

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This paper cites A compact review of molecular property prediction with graph neural networks.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification A compact review of molecular property prediction with graph neural networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:24:22.834651Z

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This paper cites Representation learning on graphs with jumping knowledge networks.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Representation learning on graphs with jumping knowledge networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:24:22.825428Z

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This paper cites A Survey of Graph Transformers: Architectures, Theories and Applications.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification A Survey of Graph Transformers: Architectures, Theories and Applications

Reference 54

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This paper cites The expressive power of graph neural networks: A survey.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification The expressive power of graph neural networks: A survey

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:24:22.816653Z

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This paper cites Mcdiarmid-type inequalities for graph-dependent variables and stability bounds.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Mcdiarmid-type inequalities for graph-dependent variables and stability bounds

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:24:22.806725Z

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This paper cites Interpreting and unifying graph neural networks with an optimization framework.

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification Interpreting and unifying graph neural networks with an optimization framework

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:24:22.796820Z

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