Paper Citation Record · LEDGER

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization

As of 10 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.05263.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.05263 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:35:49.754751Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4bf87971-a5af-482d-ac41-e1ec37866fe8 · outbound

This paper cites Spin glasses: Experimental facts, theoretical concepts, and open questions.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Spin glasses: Experimental facts, theoretical concepts, and open questions

Reference 1

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Observation d067df9c-3573-49ea-8540-05689d410060 · outbound

This paper cites The physics of amorphous solids.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization The physics of amorphous solids

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:35:57.364742Z

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Observation 5de7c5f8-a987-41cc-a2cf-4dbc1398b239 · outbound

This paper cites Percolation on complex networks: Theory and application.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Percolation on complex networks: Theory and application

Reference 3

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This paper cites Absence of diffusion in certain random lattices.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Absence of diffusion in certain random lattices

Reference 4

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This paper cites The jamming transition and the marginally jammed solid.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization The jamming transition and the marginally jammed solid

Reference 5

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This paper cites Neural networks and physical systems with emergent collective com- putational abilities.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Neural networks and physical systems with emergent collective com- putational abilities

Reference 6

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Observation 91ae7f75-0bfe-4a92-8d24-d65a941ac2d1 · outbound

This paper cites Chaos in random neural networks.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Chaos in random neural networks

Reference 7

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Observation e0451edf-06f0-4793-8d20-0b70319049fc · outbound

This paper cites Comparing dy- namics: Deep neural networks versus glassy systems.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Comparing dy- namics: Deep neural networks versus glassy systems

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:35:55.864980Z

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Observation d876fc6d-381b-4898-b428-b5ccd3d0b6c8 · outbound

This paper cites Spin-glass models of neural networks.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Spin-glass models of neural networks

Reference 9

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Observation 7838fbb3-100e-46f0-a286-d71ce97c1fcb · outbound

This paper cites Geometry of neural network loss surfaces via random matrix theory.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Geometry of neural network loss surfaces via random matrix theory

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:35:55.477585Z

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Observation 17935dd7-b62f-44d1-a37d-0ef9aa780d3d · outbound

This paper cites Statistical mechanics of deep learning.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Statistical mechanics of deep learning

Reference 11

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Observation bab81740-fe10-4bd4-a091-6300a64f8df4 · outbound

This paper cites Statistical physics of deep neural networks: Initialization toward optimal channels.Physical Review Research, 5(2):023023, 2023.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Statistical physics of deep neural networks: Initialization toward optimal channels.Physical Review Research, 5(2):023023, 2023

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:35:54.878512Z

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Observation 412f48f5-3fc2-4a0c-965f-fb1434bcd5be · outbound

This paper cites Dynamic neuron approach to deep neural networks: Decoupling neurons for renormalization group analysis.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Dynamic neuron approach to deep neural networks: Decoupling neurons for renormalization group analysis

Reference 13

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Observation dfc48ff7-d2c6-40fa-b501-5d2884e9c799 · outbound

This paper cites Scaling Laws for Neural Language Models.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Scaling Laws for Neural Language Models

Reference 14

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This paper cites A phase transition between positional and semantic learning in a solvable model of dot-product attention.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization A phase transition between positional and semantic learning in a solvable model of dot-product attention

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:35:54.604904Z

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Observation 0091626b-dead-4767-bf5a-465b4f2f9132 · outbound

This paper cites Grokking as a First Order Phase Transition in Two Layer Networks.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Grokking as a First Order Phase Transition in Two Layer Networks

Reference 16

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Observation 430e0c65-9d5b-47a3-8ac8-25c2b8c335d7 · outbound

This paper cites Sampling with flows, diffusion, and autoregressive neural networks from a spin-glass perspective.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Sampling with flows, diffusion, and autoregressive neural networks from a spin-glass perspective

Reference 17

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Observation 7a0af469-14ff-48b8-ba38-41c9fe57fa34 · outbound

This paper cites Statistical physics analysis of graph neural net- works: Approaching optimality in the contextual stochastic block model.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Statistical physics analysis of graph neural net- works: Approaching optimality in the contextual stochastic block model

Reference 18

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Observation 9bd17c51-a497-4dc2-8d5b-a0f2f569c36f · outbound

This paper cites Graph neural networks for social recommendation.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Graph neural networks for social recommendation

Reference 19

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Observation 8bd983f8-b77b-45f6-a983-4a85da3bf806 · outbound

This paper cites Learning from Protein Structure with Geometric Vector Perceptrons.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Learning from Protein Structure with Geometric Vector Perceptrons

Reference 20

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This paper cites Graph neu- ral networks for materials science and chemistry.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Graph neu- ral networks for materials science and chemistry

Reference 21

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Observation 96892fdf-eee3-4be7-a84c-944b2e4b2dbf · outbound

This paper cites Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 22

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

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Measuring and relieving the over-smoothing problem for graph neural networks from the topological view

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:35:53.786814Z

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This paper cites Deepgcns: Can gcns go as deep as cnns? In Proceedings of the IEEE/CVF international conference on computer vision, pages 9267–9276, 2019.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Deepgcns: Can gcns go as deep as cnns? In Proceedings of the IEEE/CVF international conference on computer vision, pages 9267–9276, 2019

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:35:53.523835Z

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Observation a2317651-03eb-4ccd-85e7-357142e9c707 · outbound

This paper cites DropEdge: Towards Deep Graph Convolutional Networks on Node Classification.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

Reference 25

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This paper cites Pde-gcn: Novel architectures for graph neural networks motivated by partial differential equations.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Pde-gcn: Novel architectures for graph neural networks motivated by partial differential equations

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:35:53.255853Z

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This paper cites Fifty years of anderson localization.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Fifty years of anderson localization

Reference 27

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Observation cfdad4b1-7d80-4ba9-b2ab-32e0076a5392 · outbound

This paper cites Direct observation of anderson localization of matter waves in a controlled disorder.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Direct observation of anderson localization of matter waves in a controlled disorder

Reference 28

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This paper cites Many-body localization and thermalization in quantum statistical mechanics.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Many-body localization and thermalization in quantum statistical mechanics

Reference 29

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This paper cites Interplay of non- hermitian skin effects and anderson localization in nonreciprocal quasiperiodic lattices.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Interplay of non- hermitian skin effects and anderson localization in nonreciprocal quasiperiodic lattices

Reference 30

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Observation 7cadf28f-6fdf-4f20-9db6-892bd5884caa · outbound

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

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Semi-Supervised Classification with Graph Convolutional Networks

Reference 31

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Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:35:51.794750Z

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Observation 3bca706b-22c0-4ce8-bfbe-7eed069d6955 · outbound

This paper cites Bridging the gap between spatial and spectral domains: A unified framework for graph neural networks.

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization Bridging the gap between spatial and spectral domains: A unified framework for graph neural networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:35:51.554753Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.