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Approximating Average Parameters of Graphs In Memory of Shimon Even (1935-2004)

机译:Shimon Even(1935-2004)的内存中图的近似平均参数

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摘要

Inspired by Feige (36th STOC, 2004), we initiate a study of sublinear randomized algorithms for approximating average parameters of a graph. Specifically, we consider the average degree of a graph and the average distance between pairs of vertices in a graph. Since our focus is on sublinear algorithms, these algorithms access the input graph via queries to an adequate oracle. We consider two types of queries. The first type is standard neighborhood queries (i.e., what is the i~(th) neighbor of vertex v?), whereas the second type are queries regarding the quantities that we need to find the average of (i.e., what is the degree of vertex v? and what is the distance between u and v?, respectively). Loosely speaking, our results indicate a difference between the two problems: For approximating the average degree, the standard neighbor queries suffice and in fact are preferable to degree queries. In contrast, for approximating average distances, the standard neighbor queries are of little help whereas distance queries are crucial.
机译:受Feige(第36 STOC,2004年)的启发,我们开始研究亚线性随机算法,用于近似图的平均参数。具体来说,我们考虑图的平均程度和图中顶点对之间的平均距离。由于我们专注于亚线性算法,因此这些算法通过查询来访问输入图并查询适当的预言。我们考虑两种类型的查询。第一种类型是标准邻域查询(即,顶点v的第i个邻居)是什么,而第二种类型是关于我们需要求平均值的数量的查询(即,顶点的度数是多少)。顶点v?以及u和v?之间的距离分别是多少)。松散地说,我们的结果表明了两个问题之间的区别:为了逼近平均程度,标准的邻居查询就足够了,实际上比程度查询更可取。相反,对于近似平均距离,标准邻居查询几乎没有帮助,而距离查询则至关重要。

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