Paper Citation Record · LEDGER

Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:1905.10947.

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

pith.paper-citation-record.v1
1905.10947 v5

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:12:20.847332Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:49:41.899171Z

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External citation measurements

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Outbound references

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Pith citing papers

Observation d0489925-a3fb-4ae3-a2e6-725fd5c76941 · 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 Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 29

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Observation 98d9f9e5-e3fe-4e50-8ce7-0ac3b0a4e488 · inbound

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

What makes a good feedforward computational graph? Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T14:33:15.983158Z

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Observation ae70f1e7-a9e6-4238-ad1d-709bfc889cc0 · inbound

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling cites this paper.

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T14:32:41.888879Z

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Observation 1f8f8c7b-8277-415a-bc60-5bbf9a152907 · inbound

Towards Efficient Few-shot Graph Neural Architecture Search via Partitioning Gradient Contribution cites this paper.

Towards Efficient Few-shot Graph Neural Architecture Search via Partitioning Gradient Contribution Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:54:11.602452Z

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Observation a24592a4-98f9-43ad-b380-3fc4b485e629 · inbound

GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations cites this paper.

GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T00:29:33.289341Z

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

Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization cites this paper.

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

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:35:48.251490Z

Source-reported events for the cited work

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Observation 90431217-3237-4bda-8600-f6ebd509baf4 · inbound

Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification cites this paper.

Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T19:36:20.539582Z

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Observation 51e26ebb-d9e4-4a76-add2-b61e4d994463 · inbound

Effects of relational graph modularity and depth on the learning performance of neural networks cites this paper.

Effects of relational graph modularity and depth on the learning performance of neural networks Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:42.030441Z

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Observation 0b75a29d-45e1-473b-bb19-8a02e2d290ed · inbound

Player-Team Heterogeneous Interaction Graph Transformer for Soccer Outcome Prediction cites this paper.

Player-Team Heterogeneous Interaction Graph Transformer for Soccer Outcome Prediction Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T17:49:10.681298Z

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Observation b7ac330c-8711-4187-82df-2539b58e4b35 · inbound

GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative Spacecraft cites this paper.

GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative Spacecraft Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:31.395073Z

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Observation 4831873d-2228-4d57-b9d2-8ebfea69ace0 · inbound

Graph Neural Network Approach to Predicting Magnetization in Quasi-One-Dimensional Ising Systems cites this paper.

Graph Neural Network Approach to Predicting Magnetization in Quasi-One-Dimensional Ising Systems Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T14:52:18.921283Z

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Observation b2900aa6-760c-4025-a7fb-5c3c8dbb1e96 · 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" Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-05T10:25:45.791745Z

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Observation f0422122-4f74-4997-bcd0-c8a7158878da · inbound

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias cites this paper.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:59.999444Z

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Observation 163dde9e-e773-4dad-a7a4-84809d5fcb24 · inbound

RopeDreamer: A Kinematic Recurrent State Space Model for Dynamics of Flexible Deformable Linear Objects cites this paper.

RopeDreamer: A Kinematic Recurrent State Space Model for Dynamics of Flexible Deformable Linear Objects Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:30.217002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3392e40c-5236-42f6-9d2e-74523992d962 · inbound

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

Topology-Preserving Neural Operator Learning via Hodge Decomposition Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:07:51.605407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 83d6a3cb-f12b-4358-861e-b64ce98beca8 · inbound

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

Topology-Preserving Neural Operator Learning via Hodge Decomposition Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:45:05.596078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a6f31488-bdd6-4451-8f7d-66628280dc4e · inbound

Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs cites this paper.

Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-06-30T14:54:45.622402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 59782fb3-b51c-460e-8228-5ed2842729a9 · inbound

Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation cites this paper.

Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:06:24.785977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 74619582-170e-4ae7-9ce6-6bfd611fa032 · inbound

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 118

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:37:19.112966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7446abfc-21c0-430d-9d63-41d0bd5873f8 · inbound

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

Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 288

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:49:41.900645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 554bced2-68f2-4f61-b779-38982d506397 · inbound

Distance-Preserving Embeddings in Inhomogeneous Random Graphs cites this paper.

Distance-Preserving Embeddings in Inhomogeneous Random Graphs Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 216

Resolution: unresolved; no resolver link, observed 2026-07-14T00:37:04.989965Z

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Observation 47dc7cae-6768-42ec-b7b6-0ef5b47f9dd2 · 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 Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T22:29:30.061455Z

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Unavailable: canonical work link unavailable.