Fortifying Robustness in Graph Neural Networks: A Loss Correction Approach to Mitigate Label Noise.
- DOI
- 10.1109/tnnls.2026.3661886
- Published
- 2026 Sep
- Container
- IEEE transactions on neural networks and learning systems
- Publisher
- Not recorded
- Open access
- unknown
Credibility signals
limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2026.3661886,
title = {Fortifying Robustness in Graph Neural Networks: A Loss Correction Approach to Mitigate Label Noise.},
author = {Hsieh IC and Li CT},
year = {2026},
journal = {IEEE transactions on neural networks and learning systems},
doi = {10.1109/tnnls.2026.3661886},
url = {https://doi.org/10.1109/tnnls.2026.3661886}
}RIS
TY - JOUR TI - Fortifying Robustness in Graph Neural Networks: A Loss Correction Approach to Mitigate Label Noise. AU - Hsieh IC AU - Li CT PY - 2026 JO - IEEE transactions on neural networks and learning systems DO - 10.1109/tnnls.2026.3661886 UR - https://doi.org/10.1109/tnnls.2026.3661886 ER -
APA
IC, H., & CT, L. (2026). Fortifying Robustness in Graph Neural Networks: A Loss Correction Approach to Mitigate Label Noise.. IEEE transactions on neural networks and learning systems. https://doi.org/10.1109/tnnls.2026.3661886
Source records
- pubmed · retrieved 2026-09-25T05:28:32.855Z