Paper Citation Record · LEDGER

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification

As of 10 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2502.00716.

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

pith.paper-citation-record.v1
2502.00716 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:05:17.453090Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

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

28 of 28 outbound references displayed

verified exact2
verified fuzzy16
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 747d62e5-8f5c-4e55-8be8-9c859e64d4bb · outbound

This paper cites Repeating the process, we arrive at mkuk mk + uk ˆRmi+ui (H) ≤ 1 λ log 2d · Eϵ exp M λ mk+ukX i=1 ϵiXi !!.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Repeating the process, we arrive at mkuk mk + uk ˆRmi+ui (H) ≤ 1 λ log 2d · Eϵ exp M λ mk+ukX i=1 ϵiXi !!

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.730123Z

Source-reported events for the cited work

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

Observation 91917799-f16e-44f1-b803-5d222cf6a8ca · outbound

This paper cites an unresolved cited work.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:05:17.738370Z

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

Observation ba10fcc8-4b67-43cd-bb90-9ffdb43d6f37 · outbound

This paper cites an unresolved cited work.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:05:17.825009Z

Source-reported events for the cited work

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

Observation e6764ced-0d67-491f-a610-b1e5df58d8fe · outbound

This paper cites Table 6: Dataset statistics.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Table 6: Dataset statistics

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.720806Z

Source-reported events for the cited work

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

Observation f6a8c5f7-4b14-45e8-b397-e53345fb6310 · outbound

This paper cites Experiments Setup UPL contains two tunable hyperparameters used for the pseudo labels threshold and three fixed ones which have approximated using a few experiments.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Experiments Setup UPL contains two tunable hyperparameters used for the pseudo labels threshold and three fixed ones which have approximated using a few experiments

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.712617Z

Source-reported events for the cited work

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

Observation 3adf41ce-20e4-4179-b877-f9b173b1121f · outbound

This paper cites Confidence may cheat: Self-training on graph neural networks under distribution shift.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Confidence may cheat: Self-training on graph neural networks under distribution shift

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.809282Z

Source-reported events for the cited work

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

Observation fb267d51-b363-4800-af58-899d97221a32 · outbound

This paper cites E., Rahmani, A.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification E., Rahmani, A

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.801356Z

Source-reported events for the cited work

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

Observation a4d9881d-2069-443b-b41f-a1609a84f3df · outbound

This paper cites Towards Understanding the Generalization of Graph Neural Networks.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Towards Understanding the Generalization of Graph Neural Networks

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-09T18:05:17.633565Z

Source-reported events for the cited work

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

Observation e3097d0e-9afd-4493-9e51-9504551b200e · outbound

This paper cites Effective-aggregation graph convolutional network for imbalanced classifica- tion.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Effective-aggregation graph convolutional network for imbalanced classifica- tion

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.793503Z

Source-reported events for the cited work

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

Observation 4e34f890-cc89-40d6-900e-96d44bd16014 · outbound

This paper cites Unreal: Unlabeled nodes retrieval and labeling for heavily-imbalanced node classification.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Unreal: Unlabeled nodes retrieval and labeling for heavily-imbalanced node classification

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T18:05:17.418739Z

Source-reported events for the cited work

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Observation 8887cc2f-4d3f-4fef-b0d8-c9cead3ca791 · outbound

This paper cites Flexmatch: Boosting semi- supervised learning with curriculum pseudo labeling.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Flexmatch: Boosting semi- supervised learning with curriculum pseudo labeling

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.785303Z

Source-reported events for the cited work

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

Observation 5dd6d702-3e98-4ada-a030-f31f792026c0 · outbound

This paper cites an unresolved cited work.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:05:17.777973Z

Source-reported events for the cited work

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

Observation 3e20a037-f65d-48cf-8504-c4cda2664794 · outbound

This paper cites (Kou et al.,.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification (Kou et al.,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.770626Z

Source-reported events for the cited work

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

Observation e0641c0a-9a9b-4fda-bfd5-ded3ef3b5ae8 · outbound

This paper cites The study by Pham et al.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification The study by Pham et al

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.763245Z

Source-reported events for the cited work

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

Observation 074a2214-51c0-4fa4-81dd-133e5ebe65d5 · outbound

This paper cites In contrast, our work is focused on transductive node classification, and we employed the Pseudo-labels to mitigate the imbalance effect.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification In contrast, our work is focused on transductive node classification, and we employed the Pseudo-labels to mitigate the imbalance effect

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.755287Z

Source-reported events for the cited work

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

Observation cf706847-7693-4c3d-8748-5bca50161e28 · outbound

This paper cites Both Sk and t are fixed to 100, and the quantile for uncertainty-based node selection is chosen from {Q0.7, Q0.8, Q0.9}.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Both Sk and t are fixed to 100, and the quantile for uncertainty-based node selection is chosen from {Q0.7, Q0.8, Q0.9}

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.703937Z

Source-reported events for the cited work

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

Observation b2af7b1a-25b0-42b5-b06f-361cb6088b2e · outbound

This paper cites Training: To choose the training mask, we use the training masks provided by Pytorch Geometric.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Training: To choose the training mask, we use the training masks provided by Pytorch Geometric

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.694728Z

Source-reported events for the cited work

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

Observation 13f4625e-175b-43f4-86d1-48eafb993cde · outbound

This paper cites an unresolved cited work.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-09T18:05:17.686111Z

Source-reported events for the cited work

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

Observation 0b88d090-714b-4ac4-98f3-cd3d50e8b752 · outbound

This paper cites V ., Lazarevic, A., Hall, L.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification V ., Lazarevic, A., Hall, L

Reference 2002

Resolution: unresolved; no resolver link, observed 2026-08-09T18:05:17.373318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e52d5bf0-405f-43bb-a505-8e40fb18a3d3 · outbound

This paper cites Active Learning for Graph Embedding.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Active Learning for Graph Embedding

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-09T18:05:17.369283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05c266dc-c7c8-408c-9b37-87291d0ab68d · outbound

This paper cites This imbalance often presents challenges, as many classifiers tend to prefer the majority class, sometimes to the extent of completely overlooking the minority class.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification This imbalance often presents challenges, as many classifiers tend to prefer the majority class, sometimes to the extent of completely overlooking the minority class

Reference 2006

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.746675Z

Source-reported events for the cited work

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

Observation 4d4ccef5-822a-4df8-a7d1-2535dec7b8ea · outbound

This paper cites A., and Li, S.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification A., and Li, S

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.832871Z

Source-reported events for the cited work

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

Observation 9b4d6a57-5f36-424c-bf59-8dc9be24494f · outbound

This paper cites and Pechyony, D.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification and Pechyony, D

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.840710Z

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

Observation fd771f73-3b96-47dd-b215-eabf8365b514 · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Pitfalls of Graph Neural Network Evaluation

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T18:05:17.409950Z

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Observation b7f5e3b8-4669-495c-9ee0-a4d44aeccc1c · outbound

This paper cites Meta Pseudo Labels.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Meta Pseudo Labels

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T18:05:17.404052Z

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Observation ce4976c4-81c7-42b2-98ae-4613f74e3eb4 · outbound

This paper cites Class-Imbalanced Learning on Graphs: A Survey.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Class-Imbalanced Learning on Graphs: A Survey

Reference 2022

Resolution: verified exact; local_arxiv, observed 2026-08-09T18:05:17.661131Z

Source-reported events for the cited work

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

Observation d11762d8-9ac8-4c9b-be09-f596f2ccf459 · outbound

This paper cites Laurikkala, J.

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Laurikkala, J

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:05:17.817245Z

Source-reported events for the cited work

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

Observation d827e3df-91bf-4faf-8a88-64400d104681 · outbound

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

UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification Semi-Supervised Classification with Graph Convolutional Networks

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T18:05:17.385453Z

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Unavailable: canonical work link unavailable.

Pith citing papers

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