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.

翻译：我们开发了从图中进行概率性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，例如三角形、循环或星形。我们的方法还包括针对多重性抽样和自适应聚类抽样的有限总体抽样策略，这两种方法均可重新表述为针对图节点总数的雪球抽样。除现有的随机节点或边抽样方法外，一种通用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。