标题： 从部分观测的网络数据中估计图函数 显示英文标题

标题： Graphon Estimation from Partially Observed Network Data

摘要： 我们考虑在未观察到完整网络的情况下估计由图模型生成的网络的边概率矩阵——仅观察到一些重叠的子图。 我们扩展了Zhang等人(2017)的邻域平滑(NBS)算法，以适应这种缺失数据的情况，并通过实验表明，对于广泛范围的图，扩展的NBS算法比标准的图估计算法如原始邻域平滑(NBS)、通用奇异值阈值处理(USVT)、块模型近似、矩阵补全等，显著降低了错误率。 我们还表明，扩展的NBS算法对缺失数据更加稳健。 显示英文摘要

摘要： We consider estimating the edge-probability matrix of a network generated from a graphon model when the full network is not observed---only some overlapping subgraphs are. We extend the neighbourhood smoothing (NBS) algorithm of Zhang et al. (2017) to this missing-data set-up and show experimentally that, for a wide range of graphons, the extended NBS algorithm achieves significantly smaller error rates than standard graphon estimation algorithms such as vanilla neighbourhood smoothing (NBS), universal singular value thresholding (USVT), blockmodel approximation, matrix completion, etc. We also show that the extended NBS algorithm is much more robust to missing data.