Paper Citation Record · LEDGER

Residual connections provably mitigate oversmoothing in graph neural networks

As of 11 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2501.00762.

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

pith.paper-citation-record.v1
2501.00762 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:52:03.987849Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:23:28.294391Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact1
verified fuzzy26
unresolved7
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 867c905b-5411-4193-9c3f-fd6c2c93a871 · outbound

This paper cites Random dynamical systems.

Residual connections provably mitigate oversmoothing in graph neural networks Random dynamical systems

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.806738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 315a7d89-31fb-4c39-8487-3939a7085ab0 · outbound

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

Residual connections provably mitigate oversmoothing in graph neural networks Measuring and relieving the over-smoothing problem for graph neural networks from the topological view

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.781700Z

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Observation 8a301201-d39e-4f2c-a182-227e77a32bf0 · outbound

This paper cites FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling.

Residual connections provably mitigate oversmoothing in graph neural networks FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

Reference 3

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Observation a396ab2f-d80a-4fb4-a1c3-f576e23a5fcd · outbound

This paper cites Cohen and Charles M.

Residual connections provably mitigate oversmoothing in graph neural networks Cohen and Charles M

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.762355Z

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Observation 781dfe6c-f189-4d5d-8e2f-2b4cf2617937 · outbound

This paper cites A Note on Over-Smoothing for Graph Neural Networks.

Residual connections provably mitigate oversmoothing in graph neural networks A Note on Over-Smoothing for Graph Neural Networks

Reference 5

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Source-reported events for the cited work

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Observation 9f4f37ea-958c-422f-94f9-21a321271e89 · outbound

This paper cites Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations.

Residual connections provably mitigate oversmoothing in graph neural networks Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:52:04.181485Z

Source-reported events for the cited work

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Observation f0f1ce97-6bed-4dca-91a3-f02a5b666a08 · outbound

This paper cites an unresolved cited work.

Residual connections provably mitigate oversmoothing in graph neural networks Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:52:04.740034Z

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Observation 9708a534-7b1e-4908-bc77-315d4b12c9d7 · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks.

Residual connections provably mitigate oversmoothing in graph neural networks Understanding the difficulty of training deep feedforward neural networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.718346Z

Source-reported events for the cited work

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Observation c09e8efa-0931-420b-bc75-e3fee0ead939 · outbound

This paper cites Exact combinatorial optimization with graph convolutional neural networks.

Residual connections provably mitigate oversmoothing in graph neural networks Exact combinatorial optimization with graph convolutional neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.695628Z

Source-reported events for the cited work

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Observation 4f6e8803-4c52-4eb8-acf9-e443c47d4744 · outbound

This paper cites Neural message passing for quantum chemistry.

Residual connections provably mitigate oversmoothing in graph neural networks Neural message passing for quantum chemistry

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.674603Z

Source-reported events for the cited work

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Observation 1933447d-7d60-491a-bd43-f758c92a904d · outbound

This paper cites An overview on the application of graph neural networks in wireless networks.

Residual connections provably mitigate oversmoothing in graph neural networks An overview on the application of graph neural networks in wireless networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.650229Z

Source-reported events for the cited work

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Observation 1a71b996-82a8-46b2-a2cc-545191339528 · outbound

This paper cites Deep residual learning for image recognition.

Residual connections provably mitigate oversmoothing in graph neural networks Deep residual learning for image recognition

Reference 12

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Source-reported events for the cited work

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Observation 15696b80-0dcc-464d-90d6-d4393c9e0e03 · outbound

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

Residual connections provably mitigate oversmoothing in graph neural networks Not too little, not too much: a theoretical analysis of graph (over) smoothing

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.614286Z

Source-reported events for the cited work

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Observation 60b7afe9-ea9e-48e2-81c6-f27f545fe985 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Residual connections provably mitigate oversmoothing in graph neural networks Adam: A Method for Stochastic Optimization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:03.834144Z

Source-reported events for the cited work

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Observation 7de468ea-d72a-487f-9ee0-2499b246eecb · outbound

This paper cites Kipf and Max Welling.

Residual connections provably mitigate oversmoothing in graph neural networks Kipf and Max Welling

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.597437Z

Source-reported events for the cited work

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Observation a4d343a1-14bc-4ef2-af59-c8e72d4f49b9 · outbound

This paper cites A review of graph neural networks and their applications in power systems.

Residual connections provably mitigate oversmoothing in graph neural networks A review of graph neural networks and their applications in power systems

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.578806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 18950c49-747b-4e4b-ae9d-083862e11253 · outbound

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

Residual connections provably mitigate oversmoothing in graph neural networks Deeper insights into graph convolutional networks for semi-supervised learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.560818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8ede011c-4196-40da-aa4f-d74270bbf1fe · outbound

This paper cites Graph neural networks meet wireless communications: Motivation, applications, and future directions.

Residual connections provably mitigate oversmoothing in graph neural networks Graph neural networks meet wireless communications: Motivation, applications, and future directions

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.543190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6673c75-e290-4839-9197-f2d52ace16c0 · outbound

This paper cites Markov chains.

Residual connections provably mitigate oversmoothing in graph neural networks Markov chains

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.525686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8c375bec-b1ca-494d-90ca-1930f8262fd3 · outbound

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

Residual connections provably mitigate oversmoothing in graph neural networks Graph neural networks exponentially lose expressive power for node classification

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.507940Z

Source-reported events for the cited work

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Observation c047bc25-054d-4ce5-9a03-88dc3f126ee3 · outbound

This paper cites A multiplicative ergodic theorem.

Residual connections provably mitigate oversmoothing in graph neural networks A multiplicative ergodic theorem

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.488566Z

Source-reported events for the cited work

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Observation 24a6a041-25b4-46df-a8c3-ff540e625fad · outbound

This paper cites A survey on oversmoothing in graph neural networks.

Residual connections provably mitigate oversmoothing in graph neural networks A survey on oversmoothing in graph neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.468474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c6233368-ebb7-4b32-b447-90956627bc4e · outbound

This paper cites Graph neural networks in particle physics.

Residual connections provably mitigate oversmoothing in graph neural networks Graph neural networks in particle physics

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.447773Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 875c8949-b98d-4d21-84aa-b46b7b1fc1e2 · outbound

This paper cites The graph neural network model.

Residual connections provably mitigate oversmoothing in graph neural networks The graph neural network model

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.429295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 71b030a6-0758-4da7-a612-c597371a0a35 · outbound

This paper cites Residual connections and normalization can provably prevent oversmoothing in gnns.

Residual connections provably mitigate oversmoothing in graph neural networks Residual connections and normalization can provably prevent oversmoothing in gnns

Reference 25

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Observation 65bc648a-ab8a-48bb-bebd-d882fc0b8888 · outbound

This paper cites Graph attention networks.

Residual connections provably mitigate oversmoothing in graph neural networks Graph attention networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.407828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3670a408-17a1-42c4-bc1b-4a2b873eb0fd · outbound

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

Residual connections provably mitigate oversmoothing in graph neural networks Demystifying oversmoothing in attention-based graph neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.386202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f6606020-5569-4c48-89d0-ccacaee51239 · outbound

This paper cites A non-asymptotic analysis of oversmoothing in graph neural networks.

Residual connections provably mitigate oversmoothing in graph neural networks A non-asymptotic analysis of oversmoothing in graph neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.368193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 636e60b8-d948-425b-a056-6ebb7a36c2fa · outbound

This paper cites A comprehensive survey on graph neural networks.

Residual connections provably mitigate oversmoothing in graph neural networks A comprehensive survey on graph neural networks

Reference 29

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Observation d1be6ee9-f6a4-481c-9a65-ae6a9bfe6e84 · outbound

This paper cites A review on graph neural network methods in financial applications.

Residual connections provably mitigate oversmoothing in graph neural networks A review on graph neural network methods in financial applications

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.335049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1306f931-9602-4fe4-8281-18c21a2baed1 · outbound

This paper cites Revisiting semi-supervised learning with graph embeddings.

Residual connections provably mitigate oversmoothing in graph neural networks Revisiting semi-supervised learning with graph embeddings

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.316918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b6ab79bc-ffae-4800-9087-0c5ea8f73e92 · outbound

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

Residual connections provably mitigate oversmoothing in graph neural networks Graph neural networks: A review of methods and applications

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.299425Z

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Observation c1ea89ed-579f-477d-b496-92149a7eaa11 · outbound

This paper cites Graph neural networks and their current applications in bioinformatics.

Residual connections provably mitigate oversmoothing in graph neural networks Graph neural networks and their current applications in bioinformatics

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.277594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 42004854-4883-402e-b242-20d199616ea1 · outbound

This paper cites A comprehensive review of the oversmoothing in graph neural networks.

Residual connections provably mitigate oversmoothing in graph neural networks A comprehensive review of the oversmoothing in graph neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:04.256269Z

Source-reported events for the cited work

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Pith citing papers

Observation 014ed71f-aa82-492f-8e19-0bdd56b9d305 · inbound

Critical attention scaling in long-context transformers cites this paper.

Critical attention scaling in long-context transformers Residual connections provably mitigate oversmoothing in graph neural networks

Reference 4

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Observation 44471e30-fde5-43d4-bb4e-50badf061aaa · inbound

On the Diverse Dynamical Behaviors Arising in Deep Linear Transformers cites this paper.

On the Diverse Dynamical Behaviors Arising in Deep Linear Transformers Residual connections provably mitigate oversmoothing in graph neural networks

Reference 29

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Source-reported events for the cited work

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