A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks | Paper Citation Record

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This paper cites Entrywise eigenvector analysis of random matrices with low expected rank.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Entrywise eigenvector analysis of random matrices with low expected rank

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This paper cites Convergence rates of neural networks for supervised learning on manifolds.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Convergence rates of neural networks for supervised learning on manifolds

Reference 2

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This paper cites Layer Normalization.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Layer Normalization

Reference 3

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This paper cites A note on over-smoothing for graph neural networks.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks A note on over-smoothing for graph neural networks

Reference 4

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This paper cites Characterization of generalizability of spike timing dependent plasticity trained spiking neural networks.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Characterization of generalizability of spike timing dependent plasticity trained spiking neural networks

Reference 5

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This paper cites Heterogeneous recurrent spiking neural network for spatio-temporal classification.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Heterogeneous recurrent spiking neural network for spatio-temporal classification

Reference 6

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This paper cites darts: Model uncertainty-aware differentiable architecture search.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks darts: Model uncertainty-aware differentiable architecture search

Reference 7

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This paper cites Brain-inspired spiking neural network for online unsupervised time series prediction.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Brain-inspired spiking neural network for online unsupervised time series prediction

Reference 8

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This paper cites Heterogeneous recurrent spiking neural network for spatio-temporal classification.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Heterogeneous recurrent spiking neural network for spatio-temporal classification

Reference 9

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This paper cites Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles

Reference 10

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This paper cites Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing

Reference 11

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Observation 3debb4d5-c295-4604-9d0c-7e88e026bdbb · outbound

This paper cites Topological Representations of Heterogeneous Learning Dynamics of Recurrent Spiking Neural Networks.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Topological Representations of Heterogeneous Learning Dynamics of Recurrent Spiking Neural Networks

Reference 12

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This paper cites A fully spiking hybrid neural network for energy-efficient object detection.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks A fully spiking hybrid neural network for energy-efficient object detection

Reference 13

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This paper cites Brain-inspired spatiotemporal processing algorithms for efficient event-based perception.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Brain-inspired spatiotemporal processing algorithms for efficient event-based perception

Reference 14

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Observation 3073045d-87e6-4f48-a5b5-9d792a189c86 · outbound

This paper cites Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN

Reference 15

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

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Measuring and relieving the over-smoothing problem for graph neural networks from the topological view

Reference 16

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This paper cites How does over-squashing affect the power of GNNs?.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks How does over-squashing affect the power of GNNs?

Reference 17

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This paper cites Graph neural networks for social recommendation.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Graph neural networks for social recommendation

Reference 18

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This paper cites Gemnet: Universal directional graph neural networks for molecules.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Gemnet: Universal directional graph neural networks for molecules

Reference 19

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This paper cites Neural message passing for quantum chemistry.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Neural message passing for quantum chemistry

Reference 20

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This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 21

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This paper cites Unsupervised 3D Object Learning through Neuron Activity aware Plasticity.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Unsupervised 3D Object Learning through Neuron Activity aware Plasticity

Reference 22

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This paper cites Not too little, not too much: a theoretical analysis of graph (over)smoothing.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Not too little, not too much: a theoretical analysis of graph (over)smoothing

Reference 23

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This paper cites Semi-supervised classification with graph convolutional networks.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Semi-supervised classification with graph convolutional networks

Reference 24

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This paper cites Deepgcns: Can gcns go as deep as cnns? Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9267--9276, 2019.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Deepgcns: Can gcns go as deep as cnns? Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9267--9276, 2019

Reference 25

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

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Deeper insights into graph convolutional networks for semi-supervised learning

Reference 26

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This paper cites Automating the construction of internet portals with machine learning.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Automating the construction of internet portals with machine learning

Reference 27

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This paper cites Using noise to probe recurrent neural network structure and prune synapses.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Using noise to probe recurrent neural network structure and prune synapses

Reference 28

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This paper cites Graph neural networks exponentially lose expressive power for node classification.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Graph neural networks exponentially lose expressive power for node classification

Reference 29

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This paper cites Graph neural networks for materials science and chemistry.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Graph neural networks for materials science and chemistry

Reference 30

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This paper cites Simplifying the Theory on Over-Smoothing.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Simplifying the Theory on Over-Smoothing

Reference 31

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This paper cites Graph-coupled oscillator networks.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Graph-coupled oscillator networks

Reference 32

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Observation 43bc2ec7-0d2f-4a3b-a310-45ab716bd224 · outbound

This paper cites Rusch, M.M.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Rusch, M.M

Reference 33

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Observation b989d4c5-1acd-4157-aa8a-4cff56989cea · outbound

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

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks A Survey on Oversmoothing in Graph Neural Networks

Reference 34

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This paper cites Collective classification in network data.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Collective classification in network data

Reference 35

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This paper cites Webkb dataset.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Webkb dataset

Reference 36

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This paper cites Graph attention networks.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Graph attention networks

Reference 37

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This paper cites Graph attention networks.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Graph attention networks

Reference 38

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This paper cites Demystifying the weak performance of graph attention networks.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Demystifying the weak performance of graph attention networks

Reference 39

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This paper cites Demystifying oversmoothing in attention-based graph neural networks.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Demystifying oversmoothing in attention-based graph neural networks

Reference 40

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Observation 7cc01ac3-36ff-4e01-83d0-400a3f9c96ef · outbound

This paper cites A comprehensive survey on graph neural networks.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks A comprehensive survey on graph neural networks

Reference 41

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Observation 065b7739-9c25-4406-996d-e7a593a1768f · outbound

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

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Representation learning on graphs with jumping knowledge networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:52.641674Z

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This paper cites How powerful are graph neural networks? In International Conference on Learning Representations, 2019.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks How powerful are graph neural networks? In International Conference on Learning Representations, 2019

Reference 43

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This paper cites Graph convolutional neural networks for web-scale recommender systems.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Graph convolutional neural networks for web-scale recommender systems

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:52.627098Z

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This paper cites Sparsity preserving low-rank decomposition for compression of deep networks.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Sparsity preserving low-rank decomposition for compression of deep networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:52.618058Z

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Observation 0d0e8508-b28c-4537-bbae-e09aba8c1fc0 · outbound

This paper cites Towards deeper graph neural networks with differentiable group normalization.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Towards deeper graph neural networks with differentiable group normalization

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:01:52.608976Z

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Observation 67b56044-e3c6-4f57-a1af-194ffeab6ec0 · outbound

This paper cites Beyond homophily in graph neural networks: Current limitations and effective designs.

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks Beyond homophily in graph neural networks: Current limitations and effective designs

Reference 47

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