Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach | Paper Citation Record

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This paper cites Graphboot: Quantifying uncertainty in node feature learning on large networks,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Graphboot: Quantifying uncertainty in node feature learning on large networks,

Reference 1

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This paper cites Graph Attention Networks.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Graph Attention Networks

Reference 2

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This paper cites Graph self-supervised learning: A survey,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Graph self-supervised learning: A survey,

Reference 3

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

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 4

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This paper cites Accurate prediction of protein structures and interactions using a three- track neural network,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Accurate prediction of protein structures and interactions using a three- track neural network,

Reference 5

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This paper cites Siren: Sign-aware recommendation using graph neural networks,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Siren: Sign-aware recommendation using graph neural networks,

Reference 6

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This paper cites Haqjsk: Hierarchical-aligned quantum jensen-shannon kernels for graph classification,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Haqjsk: Hierarchical-aligned quantum jensen-shannon kernels for graph classification,

Reference 7

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This paper cites Concrete Problems in AI Safety.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Concrete Problems in AI Safety

Reference 8

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This paper cites Trustworthy Graph Neural Networks: Aspects, Methods and Trends.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Trustworthy Graph Neural Networks: Aspects, Methods and Trends

Reference 9

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This paper cites On calibration of modern neural networks,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach On calibration of modern neural networks,

Reference 10

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This paper cites Posterior network: Uncertainty estimation without ood samples via density-based pseudo- counts,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Posterior network: Uncertainty estimation without ood samples via density-based pseudo- counts,

Reference 11

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This paper cites Predictive uncertainty estimation via prior networks,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Predictive uncertainty estimation via prior networks,

Reference 12

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This paper cites Bayesian graph convo- lutional neural networks for semi-supervised classification,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Bayesian graph convo- lutional neural networks for semi-supervised classification,

Reference 13

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Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Unresolved cited work

Reference 14

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This paper cites Trusted multi-view classi- fication with dynamic evidential fusion,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Trusted multi-view classi- fication with dynamic evidential fusion,

Reference 15

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This paper cites Evidential deep learning to quantify classification uncertainty,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Evidential deep learning to quantify classification uncertainty,

Reference 16

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This paper cites Trusted Multi-View Classification.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Trusted Multi-View Classification

Reference 17

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This paper cites Uncertainty aware semi- supervised learning on graph data,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Uncertainty aware semi- supervised learning on graph data,

Reference 18

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This paper cites Be confident! towards trust- worthy graph neural networks via confidence calibration,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Be confident! towards trust- worthy graph neural networks via confidence calibration,

Reference 19

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This paper cites Uncertainty aware graph gaussian process for semi-supervised learning,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Uncertainty aware graph gaussian process for semi-supervised learning,

Reference 20

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This paper cites Graph posterior network: Bayesian predictive uncertainty for node classification,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Graph posterior network: Bayesian predictive uncertainty for node classification,

Reference 21

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This paper cites Com- bining graph neural networks with expert knowledge for smart contract vulnerability detection,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Com- bining graph neural networks with expert knowledge for smart contract vulnerability detection,

Reference 22

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This paper cites Geometric deep learning on graphs and manifolds using mixture model cnns,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Geometric deep learning on graphs and manifolds using mixture model cnns,

Reference 23

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This paper cites Neural message passing for quantum chemistry,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Neural message passing for quantum chemistry,

Reference 24

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This paper cites Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark

Reference 25

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

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Semi-Supervised Classification with Graph Convolutional Networks

Reference 26

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This paper cites Inductive representation learning on large graphs,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Inductive representation learning on large graphs,

Reference 27

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This paper cites Towards deeper graph neural networks,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Towards deeper graph neural networks,

Reference 28

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This paper cites Arc: A generalist graph anomaly detector with in-context learning,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Arc: A generalist graph anomaly detector with in-context learning,

Reference 29

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This paper cites Noise-resilient unsupervised graph representation learning via multi-hop feature quality estimation,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Noise-resilient unsupervised graph representation learning via multi-hop feature quality estimation,

Reference 30

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This paper cites Predict then Propagate: Graph Neural Networks meet Personalized PageRank.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Reference 31

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This paper cites Adaptive propagation graph convolutional network,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Adaptive propagation graph convolutional network,

Reference 32

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This paper cites Simplifying graph convolutional networks,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Simplifying graph convolutional networks,

Reference 33

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This paper cites SIGN: Scalable Inception Graph Neural Networks.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach SIGN: Scalable Inception Graph Neural Networks

Reference 34

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This paper cites Learning to drop: Robust graph neural network via topological denois- ing,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Learning to drop: Robust graph neural network via topological denois- ing,

Reference 35

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This paper cites Robust graph representation learning via neural sparsifica- tion,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Robust graph representation learning via neural sparsifica- tion,

Reference 36

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This paper cites Are graph convolutional networks with random weights feasible?.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Are graph convolutional networks with random weights feasible?

Reference 37

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This paper cites Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs

Reference 38

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This paper cites Neuromorphic camera denoising using graph neural network-driven transformers,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Neuromorphic camera denoising using graph neural network-driven transformers,

Reference 39

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This paper cites Interaction-aware graph neural networks for fault diagnosis of complex industrial processes,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Interaction-aware graph neural networks for fault diagnosis of complex industrial processes,

Reference 40

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This paper cites Guest editorial: Deep neural networks for graphs: Theory, models, algorithms, and applications,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Guest editorial: Deep neural networks for graphs: Theory, models, algorithms, and applications,

Reference 41

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This paper cites Self-supervision im- proves diffusion models for tabular data imputation,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Self-supervision im- proves diffusion models for tabular data imputation,

Reference 42

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This paper cites Goodat: towards test-time graph out-of-distribution detection,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Goodat: towards test-time graph out-of-distribution detection,

Reference 43

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This paper cites A label-free heterophily-guided approach for unsupervised graph fraud detection,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach A label-free heterophily-guided approach for unsupervised graph fraud detection,

Reference 44

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This paper cites Explainable uncertainty-aware convolutional recurrent neural network for irregular medical time series,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Explainable uncertainty-aware convolutional recurrent neural network for irregular medical time series,

Reference 45

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This paper cites A survey of uncertainty in deep neural networks,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach A survey of uncertainty in deep neural networks,

Reference 46

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This paper cites Correlated parameters to accurately measure uncertainty in deep neural networks,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Correlated parameters to accurately measure uncertainty in deep neural networks,

Reference 47

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This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Dropout as a bayesian approximation: Representing model uncertainty in deep learning,

Reference 48

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This paper cites Transforming neural-net output levels to probability distributions,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Transforming neural-net output levels to probability distributions,

Reference 49

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This paper cites Being a bit frequentist improves bayesian neural networks,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Being a bit frequentist improves bayesian neural networks,

Reference 50

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This paper cites Jøsang, Subjective logic.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Jøsang, Subjective logic

Reference 51

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Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Unresolved cited work

Reference 52

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This paper cites Uncovering the structural fairness in graph contrastive learning,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Uncovering the structural fairness in graph contrastive learning,

Reference 53

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This paper cites Addressing failure prediction by learning model confidence,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Addressing failure prediction by learning model confidence,

Reference 54

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Observation a3598fa7-a67d-41bc-a172-f936e0073e93 · outbound

This paper cites Multimodal dynamics: Dynamical fusion for trustworthy multimodal classification,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Multimodal dynamics: Dynamical fusion for trustworthy multimodal classification,

Reference 55

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This paper cites Measuring and relieving the over-smoothing problem for graph neural networks from the topological view,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Measuring and relieving the over-smoothing problem for graph neural networks from the topological view,

Reference 56

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

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach A Survey on Oversmoothing in Graph Neural Networks

Reference 57

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This paper cites Dirichlet energy constrained learning for deep graph neural networks,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Dirichlet energy constrained learning for deep graph neural networks,

Reference 58

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Observation de1c0c8c-d454-4ee3-a09a-896d961e5185 · outbound

This paper cites Model degradation hinders deep graph neural networks,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Model degradation hinders deep graph neural networks,

Reference 59

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This paper cites Uncertainty estima- tion using a single deep deterministic neural network,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Uncertainty estima- tion using a single deep deterministic neural network,

Reference 60

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Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Confidence-aware learning for deep neural networks,

Reference 61

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This paper cites Categories of belief fusion,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Categories of belief fusion,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:11.468509Z

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Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Uncertainty characteristics of sub- jective opinions,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:11.433136Z

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This paper cites Review of a mathematical theory of evidence,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Review of a mathematical theory of evidence,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:11.411643Z

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Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Graph attention multi-layer perceptron,

Reference 65

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Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Permutation equivari- ant graph framelets for heterophilous graph learning,

Reference 66

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Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Representation learning on graphs with jumping knowledge networks,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:11.369660Z

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This paper cites Deepgcns: Can gcns go as deep as cnns?.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Deepgcns: Can gcns go as deep as cnns?

Reference 68

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This paper cites Simple spectral graph convolution,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Simple spectral graph convolution,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:11.350444Z

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This paper cites Agnn: Alter- nating graph-regularized neural networks to alleviate over-smoothing,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Agnn: Alter- nating graph-regularized neural networks to alleviate over-smoothing,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:11.341187Z

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Observation 626b6b45-be6d-4823-adbd-1b5f07d88ca4 · outbound

This paper cites Beyond message-passing: Generalization of graph neural networks via feature perturbation for semi-supervised node classification,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Beyond message-passing: Generalization of graph neural networks via feature perturbation for semi-supervised node classification,

Reference 71

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This paper cites Uncertainty quantification of molecular property prediction with Bayesian neural networks.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Uncertainty quantification of molecular property prediction with Bayesian neural networks

Reference 72

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Observation 6cbc2f92-430d-4b9b-91cc-d6366d0b2744 · outbound

This paper cites Uncertainty-aware multi-view representation learning,.

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Uncertainty-aware multi-view representation learning,

Reference 73

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