翻訳と辞書 Words near each other ・ Degree of ionization ・ Degree of isochronous distortion ・ Degree of parallelism ・ Degree of Pocahontas ・ Degree of polarization ・ Degree of polymerization ・ Degree of reaction ・ Degree of saturation ・ Degree of saturation (traffic) ・ Degree of start-stop distortion ・ Degree of truth ・ Degree of unsaturation ・ Degree programmes of the University of Groningen ・ Degree symbol ・ Degree-constrained spanning tree ・ Degree-preserving randomization ・ Degreed ・ Degrees north ・ Degrees of Certainty ・ Degrees of Connection ・ Degrees of Eastern Orthodox monasticism ・ Degrees of freedom ・ Degrees of freedom (mechanics) ・ Degrees of freedom (physics and chemistry) ・ Degrees of freedom (statistics) ・ Degrees of freedom problem ・ Degrees of glory ・ Degrees of the University of Oxford ・ Degrees-R-Us ・ Degreeting Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Degree-preserving randomization ： ウィキペディア英語版 Degree-preserving randomization Degree Preserving Randomization is a technique used in Network Science that aims to assess whether or not variations observed in a given graph could simply be an artifact of the graph's inherent structural properties rather than properties unique to the nodes, in an observed network. ==Background== Cataloged as early as 1996, the simplest implementation of degree preserving randomization relies on a Monte Carlo algorithm that rearranges, or "rewires" the network at random such that, with a sufficient number of rewires, the network's degree distribution is identical to the initial degree distribution of the network, though the topological structure of the network has become completely distinct from the original network. Degree preserving randomization, while it has many different forms, typically takes on the form of a relatively simple approach: for any network consisting of $N$ nodes with $E$ edges, select two dyadically tied nodes. For each of these dyadic pairs, switch the edges such that the new dyadic pairs are mismatched. After a sufficient number of these mismatches, the network increasingly loses its original observed topography. As is common with algorithms based on Markov Chains, the number of iterations, or individual rewires, must occur on a given graph such that the graph is sufficiently random and distinct from the original graph is unknown, though Espinoza asserts that a safe minimum threshold is $Q$ * E, where $Q$ "is at least 100" (Espinoza). Others have provided input for this issue, including one author who states that a safe minimum may instead be at least $\backslash frac$ * ln(1/\epsilon), where epsilon lies in a range between $10^$ and $10^$, though ultimately the correct number is not presently known. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Degree-preserving randomization」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.