Fractional Heat Kernel for Semi-Supervised Graph Learning with Small Training Sample Size
The authors developed a source-driven fractional heat-kernel framework for semi-supervised graph learning to prevent oversmoothing during long diffusion times. They introduced a fixed label source compatible with the Laplacian null space, allowing the method to sustain label information and mitigate signal collapse when training data is sparse.