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arXiv 1004.0977math.PR
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随机生长树的熵和豪斯多夫维数

Entropy and Hausdorff Dimension in Random Growing Trees

Anna Rudas, Imre Péter Tóth

摘要:

研究了偏好连接模型中随机树生长的极限行为。树从一个根开始,我们根据一个依赖于已经存在的树的度分布的规则,一个接一个地随机向系统添加顶点。所谓的权函数,根据它来制定附着规则,是这样的,树中的每个顶点最多可以有K个孩子。我们定义了一个有限树的叶子上的随机测度μ的概念,它以一种自然的方式捕获了树生长的全局特性。我们证明了这个极限测度的Hausdorff维数和packing维数是相等的,并且以概率1为常数。此外,μ的局部维数几乎在每个点处都等于豪斯多夫维数。我们给出了一个明确的公式的尺寸,给定的附件规则。

英文摘要:

We investigate the limiting behavior of random tree growth in preferential attachment models. The tree stems from a root, and we add vertices to the system one-by-one at random, according to a rule which depends on the degree distribution of the already existing tree. The so-called weight function, in terms of which the rule of attachment is formulated, is such that each vertex in the tree can have at most K children. We define the concept of a certain random measure mu on the leaves of the limiting tree, which captures a global property of the tree growth in a natural way. We prove that the Hausdorff and the packing dimension of this limiting measure is equal and constant with probability one. Moreover, the local dimension of mu equals the Hausdorff dimension at mu-almost every point. We give an explicit formula for the dimension, given the rule of attachment.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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