Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
This paper proposes Transductive Sharpening (TS), a loss-level modification that minimizes prediction entropy on unlabeled nodes for semi-supervised node classification. TS achieves consistent performance gains across benchmarks without any architectural changes.
[2605.20248] Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
[Submitted on 18 May 2026]
Title:Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
View a PDF of the paper titled Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification, by Brown Zaz and 4 other authors
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Abstract:In the transductive setting, where the full graph is observed but node labels are only partially available, progress in semi-supervised node classification has largely focused on architectural innovation. In this paper, we revisit an orthogonal axis: the training objective. We start from a simple observation: transductive models produce predictions for every node during training, including nodes without labels. These unlabeled-node predictions may contain useful training signal, but standard supervised objectives discard them because no ground-truth labels are available. Inspired by the decomposition of cross-entropy into a label-dependent alignment term and a label-independent entropy term, we propose prediction confidence as a natural way to extract this signal in the absence of labels. This motivates Transductive Sharpening (TS): a loss-level modification that minimizes prediction entropy on unlabeled nodes while counterbalancing this effect on labeled nodes. We evaluate Transductive Sharpening across a wide range of node-classification benchmarks and observe consistent performance improvements without requiring any changes to the backbone architecture. Code is available at this https URL.
Comments: 19 pages, 4 figures, 17 tables
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2605.20248 [cs.LG]
(or arXiv:2605.20248v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.20248
arXiv-issued DOI via DataCite
Submission history
From: Mar Gonzàlez I Català [view email] [v1] Mon, 18 May 2026 06:47:41 UTC (1,094 KB)
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