Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification

The paper introduces Transductive Sharpening (TS), a training objective that leverages predictions on unlabeled nodes in transductive graph settings. TS minimizes prediction entropy for unlabeled nodes while balancing this effect on labeled nodes, using prediction confidence derived from cross‑entropy decomposition. Experiments across many node‑classification benchmarks show consistent performance gains without altering the underlying GNN architecture.

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