Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification

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.

Machine Learning (cs.LG)

arXiv:2605.20248 [cs.LG]

(or arXiv:2605.20248v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.20248 Focus to learn more arXiv-issued DOI via DataCite

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