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English(EN) Does Graph Compression Preserve Signal Propagation?

图压缩方法对信号传播有不同影响

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文探讨了图压缩技术对图学习中信号传播的影响。该研究在各种数据集和压缩率下，调查了两种主要方法：粗粒化（coarsening）和稀疏化（sparsification）。研究结果表明存在一种权衡：稀疏化保持了信号多样性并减少了过度平滑，但偏离了原始传播模式；而粗粒化更接近地保留了传播保真度，但导致平滑度和秩崩溃增加。该研究强调了需要考虑信号多样性和传播保真度的评估方法。 AI

排序理由 该集群包含一篇详细介绍图压缩技术新发现的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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图压缩方法对信号传播有不同影响

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该集群包含一篇详细介绍图压缩技术新发现的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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75 days old
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kawshik Banerjee, Khaled Mohammed Saifuddin ·

    图压缩是否会保留信号传播？

    arXiv:2607.23338v1 Announce Type: new Abstract: Graph compression reduces the computational cost of graph learning, but its effect on signal propagation remains largely underexplored. Existing work evaluates compression through downstream task performance or structural preservati…

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