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The paper introduces DynLP, a GPU-accelerated label propagation algorithm for efficiently updating node labels in graph-based semi-supervised learning as new data batches arrive. DynLP avoids full recalculation by focusing updates on a relevant subgraph, significantly reducing computational redundancy. Experiments on large datasets demonstrate an average 13x and up to 102x speedup compared to existing methods.
Label propagation just got a whole lot faster: DynLP achieves up to 102x speedups by intelligently updating labels on GPUs when new data arrives.
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 changes to the relevant subgraph without requiring full recalculation. By exploiting GPU architectural optimizations, our algorithm achieves on average 13x and upto 102x speedup on large-scale datasets compared to state-of-the-art approaches.
