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New fractional heat kernel improves graph learning with minimal training data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel fractional heat-kernel framework for semi-supervised graph learning, designed to improve performance even with limited training data. This method combines nonlocal propagation with sustained label information, preventing the common issue of oversmoothing. The fractional order and diffusion time are key parameters that control label propagation and spectral weighting, leading to significant accuracy improvements on datasets like Two-Moon, Cora, and CiteSeer, particularly when using only one training label per class. AI

IMPACT This research offers a new technique for improving graph-based machine learning models, particularly in scenarios with scarce labeled data, potentially enhancing performance in fields like recommendation systems and social network analysis.

RANK_REASON Academic paper detailing a new method for graph learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New fractional heat kernel improves graph learning with minimal training data

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Academic paper detailing a new method for graph learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Farid Bozorgnia, Vyacheslav Kungurtsev, Shirali Kadyrov, Mohsen Yousefnezhad ·

    Fractional Heat Kernel for Semi-Supervised Graph Learning with Small Training Sample Size

    arXiv:2510.04440v2 Announce Type: replace Abstract: We develop a source-driven fractional heat-kernel framework for semi-super\-vised graph learning that combines nonlocal propagation with sustained label information. A fixed nonzero label source compatible with the Laplacian nul…

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