DynLP: Parallel Dynamic Batch Update for Label Propagation in Semi-Supervised Learning
Semi-supervised learning aims to infer class labels using only a small fraction of labeled data. In graph-based semi-supervised learning, this is typically achieved through label propagation to predict labels of unlabeled nodes. However, in real-world applications, data often arrive incrementally in batches. Each time a new batch appears, reapplying the traditional label propagation algorithm to recompute all labels is redundant, computationally intensive, and inefficient. To address the absence of an efficient label propagation update method, we propose DynLP, a novel GPU-centric Dynamic Batched Parallel Label Propagation algorithm that performs only the necessary updates, propagating ch...
| Authors | S M Shovan, Arindam Khanda, S M Ferdous et al. |
|---|---|
| Primary category | cs.DC |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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