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arXiv 2003.094672020-03stat.MEmath.STstat.TH

图上的雪球抽样

Snowball sampling from graphs

Melike Oguz-Alper, Li-Chun Zhang

备注：: One sentence on page 17 (highlighted) of previous version was supposed to have been replaced, as in this version

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AI总结：

本文提出了图上概率T波雪球抽样的无偏策略，用于估计三角形等有限阶子图，并将多重抽样与自适应聚类抽样转化为雪球抽样，提升了图抽样的灵活性与效率。

摘要：

我们开发了图上概率T波雪球抽样的无偏策略，其中估计的关注点可能涉及有限阶子图，如三角形、环或星形。我们的方法还涵盖了针对多重抽样和自适应聚类抽样的有限总体抽样策略，这两者都可以重新转化为旨在获取图节点总数的雪球抽样。除了现有的随机节点或边抽样方法外，一般的雪球抽样理论在图抽样的范围和效率方面提供了更大的灵活性。

英文摘要：

We develop unbiased strategies to probabilistic T-wave snowball sampling from graphs, where the interest of estimation may concern finite-order subgraphs such as triangles, cycles or stars. Our approaches encompass also the finite-population sampling strategies to multiplicity sampling and adaptive cluster sampling, both of which can be recast as snowball sampling aimed at graph node totals. A general snowball sampling theory offers greater flexibility in terms of scope and efficiency of graph sampling, in addition to the existing random node or edge sampling methods.

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