Paper Citation Record · LEDGER

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction

As of 9 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2601.17469.

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

pith.paper-citation-record.v1
2601.17469 v2

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T08:21:36.145591Z

measured 15 of 15 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 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

15 of 15 outbound references displayed

verified exact0
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 63acddd6-9647-4ffb-8c87-58efc91713a3 · outbound

This paper cites Hypergraph-enhanced Dual Semi-supervised Graph Classification.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction Hypergraph-enhanced Dual Semi-supervised Graph Classification

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:34.913062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff1b5fec-d715-4dd8-a29c-c6281a36e80d · outbound

This paper cites It demonstrates the effectiveness of our pseudo-labeling approach.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction It demonstrates the effectiveness of our pseudo-labeling approach

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:36.083513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0641194-daf5-451a-af17-09a68bc5016f · outbound

This paper cites Learning on graphs under label noise.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction Learning on graphs under label noise

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:35.656407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d0a5bda-0736-461b-a632-cfeb81db175b · outbound

This paper cites an unresolved cited work.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:36.001188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5687d08-5f2b-4155-b750-bcfb341fd7bd · outbound

This paper cites an unresolved cited work.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:36.145591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c850553c-e17c-4f39-8354-22f9f82088bb · outbound

This paper cites Learning Graph Neural Networks with Noisy Labels.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction Learning Graph Neural Networks with Noisy Labels

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:35.153442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f3275d2-3069-4a30-ac31-5e6bd2f31f94 · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction Pitfalls of Graph Neural Network Evaluation

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:35.431766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae330be3-6ab8-46cb-a02a-ac98a9c8cfa0 · outbound

This paper cites In contrast, spatial-based methods involve GNNs that directly process node feature representations and their neighbors, enabling localized message passing (Gilmer et al., 2017).

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction In contrast, spatial-based methods involve GNNs that directly process node feature representations and their neighbors, enabling localized message passing (Gilmer et al., 2017)

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:35.709444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17ef1be3-8f8a-4bd4-9b3b-e7e2ac959ceb · outbound

This paper cites Diffusion Improves Graph Learning.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction Diffusion Improves Graph Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:35.011015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07943494-a227-45db-b999-ae820d1c7d4f · outbound

This paper cites Nagaraj et al.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction Nagaraj et al

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:35.779205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc817a72-df8b-420c-970a-269bc88b417d · outbound

This paper cites DivideMix: Learning with Noisy Labels as Semi-supervised Learning.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:35.096135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d24d48b1-72dc-4c12-a3e9-05c1d9b122d9 · outbound

This paper cites ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:34.846738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0316bb57-7a17-41aa-a5f6-cc643df7fc8d · outbound

This paper cites Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:35.565520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99801b5a-74cb-49e9-9e34-7266b01a61f2 · outbound

This paper cites Wu et al.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction Wu et al

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:35.904215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34e3bbb7-c89b-42e9-8af0-8191e294231d · outbound

This paper cites Making deep neural networks robust to label noise: A loss correction approach.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction Making deep neural networks robust to label noise: A loss correction approach

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T08:21:35.340360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.