Paper Citation Record · LEDGER

A Note on Over-Smoothing for Graph Neural Networks

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 36 inbound Pith citation observations for arXiv:2006.13318.

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

pith.paper-citation-record.v1
2006.13318 v1

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 36 of 36 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:33:05.487054Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:49:42.010484Z

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External citation measurements

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Outbound references

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Pith citing papers

Observation abe5615e-6397-4a23-bc90-d6eccbec1f01 · inbound

Heterogeneous Sheaf Neural Networks cites this paper.

Heterogeneous Sheaf Neural Networks A Note on Over-Smoothing for Graph Neural Networks

Reference 5

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Observation 6a73ef4b-c878-4695-a0f5-5e8f019a0011 · inbound

Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks cites this paper.

Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks A Note on Over-Smoothing for Graph Neural Networks

Reference 76

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Observation f9cfa1e7-ffcc-488d-84ae-73b143a5f091 · inbound

Towards Data-centric Machine Learning on Directed Graphs: a Survey cites this paper.

Towards Data-centric Machine Learning on Directed Graphs: a Survey A Note on Over-Smoothing for Graph Neural Networks

Reference 13

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Observation 9627d0e5-db4c-443c-ba30-aaeaec0d4d0f · inbound

Residual Hyperbolic Graph Convolution Networks cites this paper.

Residual Hyperbolic Graph Convolution Networks A Note on Over-Smoothing for Graph Neural Networks

Reference 4

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

Residual connections provably mitigate oversmoothing in graph neural networks cites this paper.

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

Reference 5

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Observation 028ba194-710a-4b4a-8a38-4e1d08b3414b · inbound

REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization cites this paper.

REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization A Note on Over-Smoothing for Graph Neural Networks

Reference 52

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Observation 2f13268f-4244-4bc2-8e2a-7d903a83346c · inbound

GRAMA: Adaptive Graph Autoregressive Moving Average Models cites this paper.

GRAMA: Adaptive Graph Autoregressive Moving Average Models A Note on Over-Smoothing for Graph Neural Networks

Reference 17

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Observation 7c1dd11d-f251-450a-be81-7e53bbfc13bf · inbound

Resolving Oversmoothing with Opinion Dissensus cites this paper.

Resolving Oversmoothing with Opinion Dissensus A Note on Over-Smoothing for Graph Neural Networks

Reference 69

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Observation 7330a03f-5771-40fe-913c-5d0fddecd321 · inbound

A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis cites this paper.

A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis A Note on Over-Smoothing for Graph Neural Networks

Reference 30

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Observation 09b6e296-c660-469f-afaf-c68360f07c8d · inbound

What makes a good feedforward computational graph? cites this paper.

What makes a good feedforward computational graph? A Note on Over-Smoothing for Graph Neural Networks

Reference 6

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Observation a414c8a2-beef-4374-a888-43a4cf08a384 · inbound

Geometric GNNs for Charged Particle Tracking at GlueX cites this paper.

Geometric GNNs for Charged Particle Tracking at GlueX A Note on Over-Smoothing for Graph Neural Networks

Reference 17

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Observation 622366f4-bb0c-4cc6-baee-655ff236e03d · inbound

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? cites this paper.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? A Note on Over-Smoothing for Graph Neural Networks

Reference 43

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Observation c15b39c8-570d-4017-bb2d-62b6545faa21 · inbound

CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning cites this paper.

CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning A Note on Over-Smoothing for Graph Neural Networks

Reference 74

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Observation 322575a8-17f1-45a1-834a-d280c63a48e1 · inbound

Bridging Theory and Practice in Link Representation with Graph Neural Networks cites this paper.

Bridging Theory and Practice in Link Representation with Graph Neural Networks A Note on Over-Smoothing for Graph Neural Networks

Reference 9

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Observation 40db5b14-6da4-4aee-b708-3ace3a9ae556 · inbound

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows cites this paper.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows A Note on Over-Smoothing for Graph Neural Networks

Reference 14

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Observation bdabdcd1-bff2-4c12-bafc-04c07181b95e · inbound

Comment on "A Note on Over-Smoothing for Graph Neural Networks" cites this paper.

Comment on "A Note on Over-Smoothing for Graph Neural Networks" A Note on Over-Smoothing for Graph Neural Networks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T10:25:45.787593Z

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Observation ad3f98c7-feac-40cd-90f5-d8017a2fe4d1 · inbound

Facet: highly efficient E(3)-equivariant networks for interatomic potentials cites this paper.

Facet: highly efficient E(3)-equivariant networks for interatomic potentials A Note on Over-Smoothing for Graph Neural Networks

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:41.165521Z

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Observation cf700b82-eb71-4765-824d-87dffa65fbd7 · inbound

How Wide and How Deep? Mitigating Over-Squashing of GNNs via Channel Capacity Constrained Estimation cites this paper.

How Wide and How Deep? Mitigating Over-Squashing of GNNs via Channel Capacity Constrained Estimation A Note on Over-Smoothing for Graph Neural Networks

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-17T23:25:28.625201Z

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Observation 26bad750-b3ec-4d1f-afbc-3a9e7700dd7c · inbound

Learning from Historical Activations in Graph Neural Networks cites this paper.

Learning from Historical Activations in Graph Neural Networks A Note on Over-Smoothing for Graph Neural Networks

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-21T17:15:25.467255Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c6b523cf-922b-4a1c-ba8b-6bc3d9b57137 · inbound

Smoothness Errors in Dynamics Models and How to Avoid Them cites this paper.

Smoothness Errors in Dynamics Models and How to Avoid Them A Note on Over-Smoothing for Graph Neural Networks

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T07:27:32.243070Z

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Observation dd899da5-6fbc-47cc-b94d-c206a2645be5 · inbound

Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors cites this paper.

Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors A Note on Over-Smoothing for Graph Neural Networks

Reference 4

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A Mechanistic Analysis of Looped Reasoning Language Models cites this paper.

A Mechanistic Analysis of Looped Reasoning Language Models A Note on Over-Smoothing for Graph Neural Networks

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:41:03.018771Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7d8c3f36-9bb2-404e-9eed-3bd7dda70c93 · inbound

Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors cites this paper.

Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors A Note on Over-Smoothing for Graph Neural Networks

Reference 263

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Observation 7a6add99-3e9c-41b5-af6f-c1cf77ab9889 · inbound

Layer Embedding Deep Fusion Graph Neural Network cites this paper.

Layer Embedding Deep Fusion Graph Neural Network A Note on Over-Smoothing for Graph Neural Networks

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:41:10.214896Z

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Observation 01bbc750-108e-45f2-ae09-0f31b308cec2 · inbound

Aspect-Aware Content-Based Recommendations for Mathematical Research Papers cites this paper.

Aspect-Aware Content-Based Recommendations for Mathematical Research Papers A Note on Over-Smoothing for Graph Neural Networks

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:46:12.998127Z

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Observation 09412db8-f76b-4fe4-b271-d250206a6b8e · inbound

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning cites this paper.

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning A Note on Over-Smoothing for Graph Neural Networks

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:56:27.392442Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8b6f1b69-1278-4e97-9229-098cf290e339 · inbound

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning cites this paper.

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning A Note on Over-Smoothing for Graph Neural Networks

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:39:10.766166Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8fb699ff-ca29-4301-bd1d-7965373fe204 · inbound

Topology-Preserving Neural Operator Learning via Hodge Decomposition cites this paper.

Topology-Preserving Neural Operator Learning via Hodge Decomposition A Note on Over-Smoothing for Graph Neural Networks

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:07:51.550122Z

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Observation 4326657b-d397-4c87-85c7-a7569310223e · inbound

Topology-Preserving Neural Operator Learning via Hodge Decomposition cites this paper.

Topology-Preserving Neural Operator Learning via Hodge Decomposition A Note on Over-Smoothing for Graph Neural Networks

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:45:05.601379Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 42019845-f612-4759-8bf2-5269099c75f1 · inbound

Neural Point-Forms cites this paper.

Neural Point-Forms A Note on Over-Smoothing for Graph Neural Networks

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-19T15:02:36.572074Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c48fbcdb-bac9-462b-8abd-88a2b610151a · inbound

Graph Hierarchical Recurrence for Long-Range Generalization cites this paper.

Graph Hierarchical Recurrence for Long-Range Generalization A Note on Over-Smoothing for Graph Neural Networks

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:08:17.756805Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1102a938-3f53-40aa-aa7d-d66f0f3feed9 · inbound

AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network cites this paper.

AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network A Note on Over-Smoothing for Graph Neural Networks

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:09:14.385842Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8b90488a-b86a-48ca-98e5-3e1de74bd5c0 · inbound

Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation cites this paper.

Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation A Note on Over-Smoothing for Graph Neural Networks

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:49:42.012875Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dcdee1e8-e9d9-4186-9584-1c53a58a0f98 · inbound

From Diffusion to Reaction-Diffusion: A Dynamical-Systems View of Oversmoothing in Hypergraph Neural Networks cites this paper.

From Diffusion to Reaction-Diffusion: A Dynamical-Systems View of Oversmoothing in Hypergraph Neural Networks A Note on Over-Smoothing for Graph Neural Networks

Reference 9

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Observation 67464bd9-51e9-4640-9d03-397e515783e7 · inbound

Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement cites this paper.

Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement A Note on Over-Smoothing for Graph Neural Networks

Reference 1

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Observation c40c02ee-b0aa-4b7a-855a-543092a66d82 · inbound

Local-Global Geometric Insights for Graph Neural Networks via Entropic Curvature cites this paper.

Local-Global Geometric Insights for Graph Neural Networks via Entropic Curvature A Note on Over-Smoothing for Graph Neural Networks

Reference 14

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