發(fā)布時(shí)間：2019-01-02 12:05

【摘要】：針對(duì)大型復(fù)雜網(wǎng)絡(luò)相關(guān)的概述問題展開了深入系統(tǒng)地研究,本文重點(diǎn)對(duì)屬性與結(jié)構(gòu)的相似度進(jìn)行了全面考量,由于用戶具有各自的選擇屬性,主要是將虛擬連接與實(shí)連接進(jìn)行有效的集成,一般而言,對(duì)于大型網(wǎng)絡(luò)數(shù)據(jù)會(huì)同時(shí)把具有相同屬性的節(jié)點(diǎn)共同放置于k個(gè)非重疊的分類上。本文主要是以屬性相似度為核心,然后將節(jié)點(diǎn)全部置于對(duì)應(yīng)的分類中,重點(diǎn)采用了虛擬圖概念,主要是圍繞屬性相似度開展的,旨在較好的劃分復(fù)雜網(wǎng)絡(luò)。另外,對(duì)子分類進(jìn)行調(diào)整的過程中借助了HB-圖,這樣可以有助于在分類結(jié)構(gòu)時(shí),對(duì)算法進(jìn)行優(yōu)化。該論文為了更好地加強(qiáng)算法的執(zhí)行效率,專門提出了諸多方法對(duì)算法加以改進(jìn)。也就是說,該論文中所采用的算法,能夠確保用戶較好地對(duì)上卷操作(Roll-up)以及下鉆操作(Drill-down)加以執(zhí)行,并且,圍繞各粒度層面為中心,對(duì)復(fù)雜網(wǎng)絡(luò)的概述過程展開全面的分析。實(shí)驗(yàn)結(jié)果表明本文提出的基于虛連接和實(shí)連接的復(fù)雜網(wǎng)絡(luò)概述算法OCNVR算法是切實(shí)可行的,較之于其他算法而言其執(zhí)行效率更加高校。
[Abstract]:This paper focuses on the comprehensive consideration of the similarity between attributes and structures, because users have their own selection attributes. The main purpose of this paper is to integrate virtual and real connections effectively. In general, for large network data, nodes with the same attributes will be placed together on k non-overlapping categories at the same time. This paper mainly takes attribute similarity as the core, then puts all nodes in the corresponding classification, and focuses on the concept of virtual graph, mainly around attribute similarity, in order to better partition the complex network. In addition, the HB- diagram is used to adjust the subclassification, which can be helpful to optimize the algorithm in the classification structure. In order to enhance the efficiency of the algorithm, this paper puts forward many methods to improve the algorithm. That is, the algorithm used in this paper can ensure that the user performs the Roll-up and Drill-down well, and is centered around the level of granularity. A comprehensive analysis of the overview process of complex networks is carried out. The experimental results show that the proposed OCNVR algorithm based on virtual connection and real connection is feasible and more efficient than other algorithms.
【作者單位】： 鄭州旅游職業(yè)學(xué)院信息工程系;河南工業(yè)大學(xué)信息科學(xué)與工程學(xué)院;
【分類號(hào)】：O157.5
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本文編號(hào)：2398465