Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning | Paper Citation Record

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This paper cites The graph neural network model.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning The graph neural network model

Reference 1

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This paper cites A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions.Journal of Big Data, 11(1):18, 2024.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions.Journal of Big Data, 11(1):18, 2024

Reference 2

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This paper cites Graph neural networks: A review of methods and applications.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Graph neural networks: A review of methods and applications

Reference 3

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This paper cites Graph neural networks for communi- cation networks: Context, use cases and opportunities.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Graph neural networks for communi- cation networks: Context, use cases and opportunities

Reference 4

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This paper cites Graph neural networks for wireless communications: From theory to practice.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Graph neural networks for wireless communications: From theory to practice

Reference 5

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This paper cites Graph-based deep learning for communication networks: A survey.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Graph-based deep learning for communication networks: A survey

Reference 6

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This paper cites Graph neural networks for affective social media: A comprehensive overview.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Graph neural networks for affective social media: A comprehensive overview

Reference 7

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This paper cites A graph neural network framework for social recommendations.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning A graph neural network framework for social recommendations

Reference 8

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This paper cites Graph Neural Networks with Continual Learning for Fake News Detection from Social Media.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Graph Neural Networks with Continual Learning for Fake News Detection from Social Media

Reference 9

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This paper cites Investigating and quantifying the repro- ducibility of graph neural networks in predictive medicine.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Investigating and quantifying the repro- ducibility of graph neural networks in predictive medicine

Reference 10

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This paper cites Disease prediction via graph neural networks.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Disease prediction via graph neural networks

Reference 11

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This paper cites Medical-knowledge-based graph neural network for medication combination prediction.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Medical-knowledge-based graph neural network for medication combination prediction

Reference 12

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This paper cites Overview of artificial neural networks.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Overview of artificial neural networks

Reference 13

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This paper cites Artificial neural networks: A tutorial.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Artificial neural networks: A tutorial

Reference 14

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This paper cites An overview on edge computing research.IEEE access, 8:85714–85728, 2020.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning An overview on edge computing research.IEEE access, 8:85714–85728, 2020

Reference 15

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This paper cites Introduction to spiking neural networks: Information processing, learning and applications.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Introduction to spiking neural networks: Information processing, learning and applications

Reference 17

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This paper cites Spiking neural networks: A survey.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Spiking neural networks: A survey

Reference 18

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This paper cites Spiking neural networks and their applica- tions: A review.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Spiking neural networks and their applica- tions: A review

Reference 19

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This paper cites A quantitative description of membrane current and its application to conduction and excitation in nerve.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning A quantitative description of membrane current and its application to conduction and excitation in nerve

Reference 20

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This paper cites The leaky integrate-and-fire neuron: A platform for synaptic model exploration on the spinnaker chip.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning The leaky integrate-and-fire neuron: A platform for synaptic model exploration on the spinnaker chip

Reference 21

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This paper cites Spiking Deep Networks with LIF Neurons.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Spiking Deep Networks with LIF Neurons

Reference 22

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This paper cites SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning

Reference 23

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This paper cites Continuous Spiking Graph Neural Networks.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Continuous Spiking Graph Neural Networks

Reference 24

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This paper cites Scaling up dynamic graph representation learning via spiking neural networks.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Scaling up dynamic graph representation learning via spiking neural networks

Reference 25

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This paper cites Simple model of spiking neurons.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Simple model of spiking neurons

Reference 26

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This paper cites The geometry of excitability and bursting, 2007.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning The geometry of excitability and bursting, 2007

Reference 27

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This paper cites Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Adaptive exponential integrate-and-fire model as an effective description of neuronal activity

Reference 28

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This paper cites Spiking neural networks for nonlinear regression.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Spiking neural networks for nonlinear regression

Reference 29

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This paper cites Event-based angular velocity regression with spiking networks.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Event-based angular velocity regression with spiking networks

Reference 30

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This paper cites Spiking Neural Operators for Scientific Machine Learning.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Spiking Neural Operators for Scientific Machine Learning

Reference 31

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This paper cites Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning

Reference 32

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This paper cites Training spiking neural networks using lessons from deep learning.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Training spiking neural networks using lessons from deep learning

Reference 33

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This paper cites Direct training for spiking neural networks: Faster, larger, better.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Direct training for spiking neural networks: Faster, larger, better

Reference 34

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This paper cites Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods

Reference 35

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This paper cites Neuroscience inspired scientific machine learning (part-1): Variable spiking neuron for regression, 2023.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Neuroscience inspired scientific machine learning (part-1): Variable spiking neuron for regression, 2023

Reference 36

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This paper cites Neuroscience inspired neural operator for partial differential equations.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Neuroscience inspired neural operator for partial differential equations

Reference 37

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This paper cites Dendritic sodium spikes are variable triggers of axonal action potentials in hippocampal ca1 pyramidal neurons.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Dendritic sodium spikes are variable triggers of axonal action potentials in hippocampal ca1 pyramidal neurons

Reference 38

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This paper cites Deuchars, and Jamie Johnston.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Deuchars, and Jamie Johnston

Reference 39

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This paper cites A survey on neuro- morphic computing: Models and hardware.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning A survey on neuro- morphic computing: Models and hardware

Reference 40

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This paper cites A review of spiking neuromorphic hardware communication systems.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning A review of spiking neuromorphic hardware communication systems

Reference 41

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This paper cites Mapping spiking neural networks to neuro- morphic hardware.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Mapping spiking neural networks to neuro- morphic hardware

Reference 42

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This paper cites A low-cost high-speed neuromorphic hardware based on spiking neural network.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning A low-cost high-speed neuromorphic hardware based on spiking neural network

Reference 43

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This paper cites Neuromorphic artificial intelligence systems.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Neuromorphic artificial intelligence systems

Reference 44

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

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Semi-Supervised Classification with Graph Convolutional Networks

Reference 45

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This paper cites Graph convolutional networks: a comprehensive review.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Graph convolutional networks: a comprehensive review

Reference 46

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This paper cites Principal neighbourhood aggregation for graph nets.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Principal neighbourhood aggregation for graph nets

Reference 47

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Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Inductive representation learning on large graphs

Reference 48

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Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Neftci, Hesham Mostafa, and Friedemann Zenke

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:09:35.481051Z

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This paper cites Superspike: Supervised learning in multilayer spiking neural networks.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Superspike: Supervised learning in multilayer spiking neural networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:09:35.463120Z

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This paper cites Hestroffer, Marie-Agathe Charpagne, Marat I.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Hestroffer, Marie-Agathe Charpagne, Marat I

Reference 51

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Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Unresolved cited work

Reference 52

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This paper cites Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials

Reference 53

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Observation a37decc0-f62e-423c-a9ce-b7aeb43e4cc6 · outbound

This paper cites Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:09:35.395742Z

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Observation edc9e680-866f-4c3f-8237-ecd44b10edfc · outbound

This paper cites Empirical evaluation of gated recur- rent neural networks on sequence modeling, 2014.

Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning Empirical evaluation of gated recur- rent neural networks on sequence modeling, 2014

Reference 55

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