Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification

2026-05-18

AI Summary

This work addresses the limitation in standard graph-based semi-supervised node classification, where the training objective disregards predictive information from unlabeled nodes and thus fails to fully exploit the complete graph structure inherent in transductive learning. The authors propose a model-agnostic loss function enhancement that decomposes cross-entropy to incorporate prediction confidence from unlabeled nodes as an auxiliary learning signal. Specifically, the method minimizes the entropy of predictions on unlabeled nodes while preserving the supervised signal from labeled nodes through a balanced optimization objective. This approach seamlessly integrates with existing graph neural network (GNN) architectures without requiring architectural modifications. Extensive experiments across multiple benchmark datasets demonstrate consistent performance improvements, confirming the method’s effectiveness and broad applicability.

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 https://github.com/transductive-sharpening/tunedGNN.

Problem

• transductive learning
• semi-supervised node classification
• unlabeled predictions
• prediction entropy
• graph neural networks

Innovation

• Transductive Sharpening
• prediction confidence
• entropy minimization
• semi-supervised node classification
• graph neural networks