Paper Citation Record · LEDGER

Rethinking Tokenized Graph Transformers for Node Classification

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2502.08101.

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

pith.paper-citation-record.v1
2502.08101 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T10:31:15.048034Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:16:57.332244Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

verified exact0
verified fuzzy36
unresolved7
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4104a7d4-c933-4f12-a045-607f5d0dd100 · outbound

This paper cites write newline.

Rethinking Tokenized Graph Transformers for Node Classification write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T10:31:14.843160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb966f44-2865-4c60-81c6-093f6086a7d5 · outbound

This paper cites V., and Galstyan, A.

Rethinking Tokenized Graph Transformers for Node Classification V., and Galstyan, A

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.714229Z

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Observation 35c34978-f833-4d9f-a173-5ed18ed090ba · outbound

This paper cites Beyond low-frequency information in graph convolutional networks.

Rethinking Tokenized Graph Transformers for Node Classification Beyond low-frequency information in graph convolutional networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.700487Z

Source-reported events for the cited work

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Observation ad40fa47-8336-4867-8798-f4d6f796b38f · outbound

This paper cites Specformer: Spectral graph neural networks meet transformers.

Rethinking Tokenized Graph Transformers for Node Classification Specformer: Spectral graph neural networks meet transformers

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.686834Z

Source-reported events for the cited work

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Observation 360ce613-83b8-4d4c-8821-ddeb6a8e7bbe · outbound

This paper cites How attentive are graph attention networks? In Proceedings of the International Conference on Learning Representations, 2022.

Rethinking Tokenized Graph Transformers for Node Classification How attentive are graph attention networks? In Proceedings of the International Conference on Learning Representations, 2022

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.672523Z

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Observation 47e15606-ff23-4303-bba2-94892e00dffb · outbound

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

Rethinking Tokenized Graph Transformers for Node Classification Measuring and relieving the over-smoothing problem for graph neural networks from the topological view

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.658063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4ce29db0-b097-4a3c-9718-9b35a47c98ea · outbound

This paper cites Nagphormer: A tokenized graph transformer for node classification in large graphs.

Rethinking Tokenized Graph Transformers for Node Classification Nagphormer: A tokenized graph transformer for node classification in large graphs

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.642114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1744a781-a106-4505-875d-cb349873bdc1 · outbound

This paper cites SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning.

Rethinking Tokenized Graph Transformers for Node Classification SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T10:31:14.885876Z

Source-reported events for the cited work

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Observation 358ba552-f495-4bc8-a087-f8b8a74b57d8 · outbound

This paper cites NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification.

Rethinking Tokenized Graph Transformers for Node Classification NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T10:31:14.890499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b02b59f0-7b3a-4045-bf42-4d6de64d370c · outbound

This paper cites Neighborhood convolutional graph neural network.

Rethinking Tokenized Graph Transformers for Node Classification Neighborhood convolutional graph neural network

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.628101Z

Source-reported events for the cited work

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Observation cf3bef8c-54ab-4750-b936-2c880df3fa80 · outbound

This paper cites Pamt: A novel propagation-based approach via adaptive similarity mask for node classification.

Rethinking Tokenized Graph Transformers for Node Classification Pamt: A novel propagation-based approach via adaptive similarity mask for node classification

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.613556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9b4b4d68-79ad-4cd9-a1b1-2cf50e262ab7 · outbound

This paper cites Nagphormer+: A tokenized graph transformer with neighborhood augmentation for node classification in large graphs.

Rethinking Tokenized Graph Transformers for Node Classification Nagphormer+: A tokenized graph transformer with neighborhood augmentation for node classification in large graphs

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.598601Z

Source-reported events for the cited work

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Observation 921f4048-6855-4e87-b3b9-926d3c607c0d · outbound

This paper cites Simple and deep graph convolutional networks.

Rethinking Tokenized Graph Transformers for Node Classification Simple and deep graph convolutional networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.585005Z

Source-reported events for the cited work

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Observation 87e15dbb-c08e-474c-8871-cbf6faa4b2cb · outbound

This paper cites Adaptive Universal Generalized PageRank Graph Neural Network.

Rethinking Tokenized Graph Transformers for Node Classification Adaptive Universal Generalized PageRank Graph Neural Network

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.570832Z

Source-reported events for the cited work

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Observation f6ecf7c6-612b-4eed-a7eb-363fbd6aba2c · outbound

This paper cites Polynormer: Polynomial-expressive graph transformer in linear time.

Rethinking Tokenized Graph Transformers for Node Classification Polynormer: Polynomial-expressive graph transformer in linear time

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.556696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bc36cabd-91ed-40f9-ab87-68f90b468303 · outbound

This paper cites Vcr-graphormer: A mini-batch graph transformer via virtual connections.

Rethinking Tokenized Graph Transformers for Node Classification Vcr-graphormer: A mini-batch graph transformer via virtual connections

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.541934Z

Source-reported events for the cited work

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Observation 704804b6-4454-4183-aca8-3de6e7b87a82 · outbound

This paper cites Block modeling-guided graph convolutional neural networks.

Rethinking Tokenized Graph Transformers for Node Classification Block modeling-guided graph convolutional neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.527551Z

Source-reported events for the cited work

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Observation efcc9f22-2ff7-41ee-90bd-fe1bc9633053 · outbound

This paper cites Structural robust label propagation on homogeneous graphs.

Rethinking Tokenized Graph Transformers for Node Classification Structural robust label propagation on homogeneous graphs

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.512579Z

Source-reported events for the cited work

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Observation c2115796-9b3e-485f-aa88-8ddbb8bca518 · outbound

This paper cites an unresolved cited work.

Rethinking Tokenized Graph Transformers for Node Classification Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-08T10:31:15.498621Z

Source-reported events for the cited work

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Observation 033a288b-b25b-424e-a539-6fa314539ff7 · outbound

This paper cites Predict then propagate: Graph neural networks meet personalized pagerank.

Rethinking Tokenized Graph Transformers for Node Classification Predict then propagate: Graph neural networks meet personalized pagerank

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.483053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1fbd2e9a-022e-4832-9939-dd9e78224ccd · outbound

This paper cites Finding global homophily in graph neural networks when meeting heterophily.

Rethinking Tokenized Graph Transformers for Node Classification Finding global homophily in graph neural networks when meeting heterophily

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.468484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 051b4028-042d-4c58-baa8-85cde0c30906 · outbound

This paper cites Revisiting heterophily for graph neural networks.

Rethinking Tokenized Graph Transformers for Node Classification Revisiting heterophily for graph neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.453508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 491604d6-fa94-497e-af92-4a4fa4b2a898 · outbound

This paper cites Polyformer: Scalable node-wise filters via polynomial graph transformer.

Rethinking Tokenized Graph Transformers for Node Classification Polyformer: Scalable node-wise filters via polynomial graph transformer

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.437884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a96293fe-3ce7-41cb-8fa5-015143e0a8d8 · outbound

This paper cites Rethinking structural encodings: Adaptive graph transformer for node classification task.

Rethinking Tokenized Graph Transformers for Node Classification Rethinking structural encodings: Adaptive graph transformer for node classification task

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.423717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8c0af025-ac45-4b2c-8ff9-3c4f355aee21 · outbound

This paper cites Co-embedding attributed networks.

Rethinking Tokenized Graph Transformers for Node Classification Co-embedding attributed networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.409613Z

Source-reported events for the cited work

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Observation 485018d8-b750-4a27-893b-21f56a555187 · outbound

This paper cites C., Lei, Y., and Yang, B.

Rethinking Tokenized Graph Transformers for Node Classification C., Lei, Y., and Yang, B

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.395562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 74e84631-ce2b-4165-a9c2-641986e8262a · outbound

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Rethinking Tokenized Graph Transformers for Node Classification Unresolved cited work

Reference 27

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Observation 8a6eb613-9f20-40e6-931f-aaca57009b3d · outbound

This paper cites P., Luu, A.

Rethinking Tokenized Graph Transformers for Node Classification P., Luu, A

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.367003Z

Source-reported events for the cited work

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Observation e14fd3dd-8dfe-4b65-9268-2375fd0d1537 · outbound

This paper cites N., Kaiser, ., and Polosukhin, I.

Rethinking Tokenized Graph Transformers for Node Classification N., Kaiser, ., and Polosukhin, I

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.352868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c5f02639-e324-4ab4-9c75-a95f305675ff · outbound

This paper cites Graph Attention Networks.

Rethinking Tokenized Graph Transformers for Node Classification Graph Attention Networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.338217Z

Source-reported events for the cited work

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Observation 97f94078-6c88-4a75-bdee-eef085affdf4 · outbound

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Rethinking Tokenized Graph Transformers for Node Classification Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-08T10:31:15.323379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f416410b-04a5-45a4-919e-b9b558cdf411 · outbound

This paper cites AM-GCN: adaptive multi-channel graph convolutional networks.

Rethinking Tokenized Graph Transformers for Node Classification AM-GCN: adaptive multi-channel graph convolutional networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.309116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c253695e-ba9d-41a6-a8f8-9730d06f2a5c · outbound

This paper cites Simplifying Graph Convolutional Networks.

Rethinking Tokenized Graph Transformers for Node Classification Simplifying Graph Convolutional Networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.293517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 192efc73-01c9-4193-970c-767f53d3d13d · outbound

This paper cites Nodeformer: A scalable graph structure learning transformer for node classification.

Rethinking Tokenized Graph Transformers for Node Classification Nodeformer: A scalable graph structure learning transformer for node classification

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.278491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a381c61b-efbb-4046-ba0b-59715ae6101b · outbound

This paper cites Simplifying and empowering transformers for large-graph representations.

Rethinking Tokenized Graph Transformers for Node Classification Simplifying and empowering transformers for large-graph representations

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.263719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba2670d2-5db5-42c5-88f6-1962741a1ef8 · outbound

This paper cites Less is more: on the over-globalizing problem in graph transformers.

Rethinking Tokenized Graph Transformers for Node Classification Less is more: on the over-globalizing problem in graph transformers

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.247283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 156d03a5-6cd7-4b4f-b95e-9e63c4a57f86 · outbound

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

Rethinking Tokenized Graph Transformers for Node Classification Representation learning on graphs with jumping knowledge networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.231170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 59c030e0-6aef-4d00-af74-f09aa658286a · outbound

This paper cites FPGNN: fair path graph neural network for mitigating discrimination.

Rethinking Tokenized Graph Transformers for Node Classification FPGNN: fair path graph neural network for mitigating discrimination

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.214018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2947c8b8-da04-42ca-8ee3-be154b61ef1c · outbound

This paper cites Learning fair representations via rebalancing graph structure.

Rethinking Tokenized Graph Transformers for Node Classification Learning fair representations via rebalancing graph structure

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.199368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 29879048-9cf7-4bd2-8a0d-4b8f772e3eef · outbound

This paper cites Disentangled contrastive learning for fair graph representations.

Rethinking Tokenized Graph Transformers for Node Classification Disentangled contrastive learning for fair graph representations

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.183509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 06709ab4-a53e-40bf-b319-36e0f3f794d7 · outbound

This paper cites Hierarchical Graph Transformer with Adaptive Node Sampling.

Rethinking Tokenized Graph Transformers for Node Classification Hierarchical Graph Transformer with Adaptive Node Sampling

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.164160Z

Source-reported events for the cited work

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Observation bed5e314-d5dd-4d90-b3df-47d5e96e7de1 · outbound

This paper cites Gophormer: Ego-Graph Transformer for Node Classification.

Rethinking Tokenized Graph Transformers for Node Classification Gophormer: Ego-Graph Transformer for Node Classification

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-08T10:31:15.042310Z

Source-reported events for the cited work

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Observation b008b53d-f147-4ded-adb4-37326efff07e · outbound

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

Rethinking Tokenized Graph Transformers for Node Classification Beyond homophily in graph neural networks: Current limitations and effective designs

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:31:15.144929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 379bbd5e-9288-44a0-9b55-775ada80e23d · inbound

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach cites this paper.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach Rethinking Tokenized Graph Transformers for Node Classification

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:57.332244Z

Source-reported events for the cited work

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