Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors | Paper Citation Record

Observation fcb48bae-4d24-464b-aa80-1f8be2876af6 · outbound

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

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Semi-Supervised Classification with Graph Convolutional Networks

Reference 1

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This paper cites Graph embedding on biomedical networks: methods, applications and evaluations,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Graph embedding on biomedical networks: methods, applications and evaluations,

Reference 2

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This paper cites A comprehensive survey on graph neural networks,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors A comprehensive survey on graph neural networks,

Reference 3

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This paper cites Ironman: Gnn-assisted design space exploration in high- level synthesis via reinforcement learning,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Ironman: Gnn-assisted design space exploration in high- level synthesis via reinforcement learning,

Reference 4

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This paper cites Build a gnn-based real-time fraud detection solution using amazon sagemaker, amazon neptune, and the deep graph library.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Build a gnn-based real-time fraud detection solution using amazon sagemaker, amazon neptune, and the deep graph library

Reference 5

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This paper cites Financial fraud detection using graph neural networks: A systematic review,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Financial fraud detection using graph neural networks: A systematic review,

Reference 6

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This paper cites CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks

Reference 7

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This paper cites Scalable Graph Learning for Anti-Money Laundering: A First Look.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Scalable Graph Learning for Anti-Money Laundering: A First Look

Reference 8

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This paper cites Enhancing graph neural network-based fraud detectors against camouflaged fraudsters,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Enhancing graph neural network-based fraud detectors against camouflaged fraudsters,

Reference 9

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This paper cites αcyber: Enhancing robustness of android malware detection system against adversarial attacks on heterogeneous graph based model,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors αcyber: Enhancing robustness of android malware detection system against adversarial attacks on heterogeneous graph based model,

Reference 10

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Observation d66ecc91-ee00-4190-b14e-483a9a3a6f13 · outbound

This paper cites Graph neural networks for hardware vulnerability analysis—can you trust your gnn?,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Graph neural networks for hardware vulnerability analysis—can you trust your gnn?,

Reference 11

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Observation f36fe6d9-adb7-49c2-b278-6fd0eec62ace · outbound

This paper cites Graph neural networks for vulnerability detection: A counterfactual explanation,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Graph neural networks for vulnerability detection: A counterfactual explanation,

Reference 12

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This paper cites Fraud Reports by Federal Trade Commission.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Fraud Reports by Federal Trade Commission

Reference 13

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This paper cites Value of fraud loss in the United States in 2022, by payment method.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Value of fraud loss in the United States in 2022, by payment method

Reference 14

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This paper cites Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead,

Reference 15

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This paper cites Neutron soft error rate measurements in a 90-nm cmos process and scaling trends in sram from 0.25-/spl mu/m to 90-nm generation,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Neutron soft error rate measurements in a 90-nm cmos process and scaling trends in sram from 0.25-/spl mu/m to 90-nm generation,

Reference 16

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This paper cites Razor: circuit-level correction of timing errors for low- power operation,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Razor: circuit-level correction of timing errors for low- power operation,

Reference 17

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This paper cites Impact of technology and voltage scaling on the soft error susceptibility in nanoscale cmos,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Impact of technology and voltage scaling on the soft error susceptibility in nanoscale cmos,

Reference 18

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This paper cites Thundervolt: enabling aggressive voltage underscaling and timing error resilience for energy efficient deep learning accelera- tors,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Thundervolt: enabling aggressive voltage underscaling and timing error resilience for energy efficient deep learning accelera- tors,

Reference 19

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This paper cites Detecting silent data corruptions in the wild.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Detecting silent data corruptions in the wild

Reference 20

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This paper cites Detection and prevention of silent data corruption in an exabyte-scale database system,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Detection and prevention of silent data corruption in an exabyte-scale database system,

Reference 21

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This paper cites An assessment of vulnerability of hardware neural networks to dynamic voltage and temperature variations,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors An assessment of vulnerability of hardware neural networks to dynamic voltage and temperature variations,

Reference 22

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This paper cites Dnn engine: A 28-nm timing-error tolerant sparse deep neural network processor for iot applications,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Dnn engine: A 28-nm timing-error tolerant sparse deep neural network processor for iot applications,

Reference 23

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This paper cites Ares: A framework for quantifying the resilience of deep neural networks,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Ares: A framework for quantifying the resilience of deep neural networks,

Reference 24

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This paper cites Bit error robustness for energy-efficient dnn accelera- tors,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Bit error robustness for energy-efficient dnn accelera- tors,

Reference 25

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This paper cites Adversarial attacks and defenses on graphs,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Adversarial attacks and defenses on graphs,

Reference 26

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This paper cites Adversarial attack and defense on graph data: A survey,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Adversarial attack and defense on graph data: A survey,

Reference 27

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This paper cites PyGFI: Analyzing and Enhancing Robustness of Graph Neural Networks Against Hardware Errors.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors PyGFI: Analyzing and Enhancing Robustness of Graph Neural Networks Against Hardware Errors

Reference 28

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Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Graph structure learning for robust graph neural networks,

Reference 29

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This paper cites Topology attack and defense for graph neural networks: an optimization perspective,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Topology attack and defense for graph neural networks: an optimization perspective,

Reference 30

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This paper cites Robustness of graph neural networks at scale,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Robustness of graph neural networks at scale,

Reference 31

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This paper cites Fast graph representation learning with PyTorch Geometric,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Fast graph representation learning with PyTorch Geometric,

Reference 32

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This paper cites How powerful are graph neural networks?,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors How powerful are graph neural networks?,

Reference 33

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This paper cites Computing graph neural networks: A survey from algorithms to accelerators,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Computing graph neural networks: A survey from algorithms to accelerators,

Reference 34

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Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Empowering graph representation learning with test-time graph transformation,

Reference 35

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This paper cites Resilient low voltage accelerators for high energy efficiency,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Resilient low voltage accelerators for high energy efficiency,

Reference 36

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This paper cites On characterizing near-threshold sram failures in finfet technology,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors On characterizing near-threshold sram failures in finfet technology,

Reference 37

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This paper cites Matic: Learning around errors for efficient low-voltage neural network accelerators,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Matic: Learning around errors for efficient low-voltage neural network accelerators,

Reference 38

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This paper cites The theory of error-correcting codes,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors The theory of error-correcting codes,

Reference 39

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This paper cites Chapter 5 - error coding techniques,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Chapter 5 - error coding techniques,

Reference 40

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This paper cites Berry: Bit error robustness for energy-efficient reinforce- ment learning-based autonomous systems,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Berry: Bit error robustness for energy-efficient reinforce- ment learning-based autonomous systems,

Reference 41

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This paper cites Mulberry: Enabling bit-error robustness for energy- efficient multi-agent autonomous systems,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Mulberry: Enabling bit-error robustness for energy- efficient multi-agent autonomous systems,

Reference 42

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This paper cites Greentpu: Improving timing error resilience of a near- threshold tensor processing unit,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Greentpu: Improving timing error resilience of a near- threshold tensor processing unit,

Reference 43

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This paper cites Read: Reliability-enhanced accelerator dataflow opti- mization using critical input pattern reduction,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Read: Reliability-enhanced accelerator dataflow opti- mization using critical input pattern reduction,

Reference 44

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This paper cites Sparse dnns with improved adversarial robustness,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Sparse dnns with improved adversarial robustness,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.952666Z

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This paper cites Reliability evaluation of pruned neural networks against errors on parameters,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Reliability evaluation of pruned neural networks against errors on parameters,

Reference 46

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This paper cites Understanding structural vulnerability in graph convo- lutional networks,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Understanding structural vulnerability in graph convo- lutional networks,

Reference 47

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Observation acdfa1dc-201f-4899-8661-51a0e35cddff · outbound

This paper cites Link prediction based on graph neural net- works,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Link prediction based on graph neural net- works,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.925421Z

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This paper cites Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking,

Reference 49

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Observation d4c30283-232d-4b6b-bb94-f32d158d8bf4 · outbound

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

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Birds of a feather: Homophily in social networks,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.905708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5864b1b6-ab04-4705-b978-f2b88730362f · outbound

This paper cites Principal neighbourhood aggregation for graph nets,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Principal neighbourhood aggregation for graph nets,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.895828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9a5b3182-4354-4263-9430-d9df8fea37f2 · outbound

This paper cites Graph Attention Networks.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Graph Attention Networks

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T18:14:08.677321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9ef8b30-1dc8-40f7-8054-abfc4eb07792 · outbound

This paper cites Revisiting semi-supervised learning with graph embed- dings,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Revisiting semi-supervised learning with graph embed- dings,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.885641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation efbcf338-82f9-4418-a0de-7aefbae51a74 · outbound

This paper cites Pubmed dataset.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Pubmed dataset

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.876180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 81dda0a7-a66e-4373-9a3f-bc51d8ae60a6 · outbound

This paper cites Subgraph matching kernels for attributed graphs,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Subgraph matching kernels for attributed graphs,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.866548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7a70ec93-0d60-43a7-ad09-98839bde6f26 · outbound

This paper cites Protein function prediction via graph kernels,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Protein function prediction via graph kernels,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.856997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 848d026f-01c9-4f6e-938c-6bc119d33ec1 · outbound

This paper cites The network data repository with interactive graph analytics and visualization,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors The network data repository with interactive graph analytics and visualization,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T18:14:08.693584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c12e6b14-9d17-4229-852c-9d6375fd401b · outbound

This paper cites Microsoft academic graph: When experts are not enough,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Microsoft academic graph: When experts are not enough,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.841943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 07693846-6665-4380-aca1-843f2da199a6 · outbound

This paper cites The extreme classification repository: Multi-label datasets and code,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors The extreme classification repository: Multi-label datasets and code,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.831288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dd68dedd-fa0d-4d04-9e55-f2dc87cba325 · outbound

This paper cites Towards sparsification of graph neural networks,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Towards sparsification of graph neural networks,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.821209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c1f630a2-ea1e-4d84-a19e-979046e3ce99 · outbound

This paper cites Hydra: Pruning adversarially robust neural networks,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Hydra: Pruning adversarially robust neural networks,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.811715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 26fe8c91-574f-4f83-bb5a-9f8adfa5e82a · outbound

This paper cites Sparsity winning twice: Better robust generalization from more efficient training,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Sparsity winning twice: Better robust generalization from more efficient training,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.801452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b8e297ce-6d0d-44e5-9a62-72ff3510b8f7 · outbound

This paper cites Time and space complexity of graph convolutional networks,.

Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors Time and space complexity of graph convolutional networks,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:14:08.791596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Reference resolution

Outbound references

Pith citing papers