论文标题
图表中的雪球抽样
Snowball sampling from graphs
论文作者
论文摘要
我们开发了无偏见的策略来从图形中进行概率的T波雪球采样，其中估计的兴趣可能涉及有限级的子图，例如三角形，周期或恒星。我们的方法还涵盖了有限播种的采样策略，用于多样性采样和自适应群集采样，这两者都可以重铸为针对图形节点总数的雪球采样。除现有的随机节点或边缘采样方法外，一般的雪球采样理论在图形采样的范围和效率方面还具有更大的灵活性。
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.
