發(fā)布時(shí)間：2018-07-12 08:13

  本文選題：社團(tuán)檢測(cè) + 有向網(wǎng)絡(luò)　； 參考：《西安理工大學(xué)》2017年碩士論文

【摘要】：社團(tuán)結(jié)構(gòu)是復(fù)雜網(wǎng)絡(luò)研究的熱點(diǎn),近些年來提出了許多社團(tuán)檢測(cè)算法,但目前提出的大多數(shù)算法主要適用于無向網(wǎng)絡(luò)的社團(tuán)結(jié)構(gòu)挖掘,對(duì)于有向網(wǎng)絡(luò)這些算法往往難以取得較好的社團(tuán)檢測(cè)效果。另外,大規(guī)模數(shù)據(jù)量的產(chǎn)生使得傳統(tǒng)的社團(tuán)檢測(cè)算法不能滿足計(jì)算效率以及數(shù)據(jù)存儲(chǔ)等方面的需求。因此,尋找大規(guī)模有向網(wǎng)絡(luò)的社團(tuán)檢測(cè)算法對(duì)于社團(tuán)的研究具有重要的意義。本文主要工作如下:(1)對(duì)復(fù)雜網(wǎng)絡(luò)的理論基礎(chǔ)、社團(tuán)的概念以及Hadoop技術(shù)進(jìn)行了詳細(xì)的介紹,分析了一些具有代表性的社團(tuán)檢測(cè)算法并指出其各自的優(yōu)缺點(diǎn);深入研究了社團(tuán)檢測(cè)的研究現(xiàn)狀和所面臨的問題。(2)研究基于相似度的有向網(wǎng)絡(luò)社團(tuán)檢測(cè)算法,該算法利用網(wǎng)絡(luò)的方向信息指導(dǎo)節(jié)點(diǎn)相似度的計(jì)算,從而把有向網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)信息轉(zhuǎn)化為代數(shù)值,然后利用相似度改進(jìn)CNM算法,結(jié)合CNM算法本身的優(yōu)點(diǎn)提高了算法的準(zhǔn)確性和適用性。(3)針對(duì)傳統(tǒng)社團(tuán)檢測(cè)算法在單機(jī)環(huán)境下不能有效處理大規(guī)模網(wǎng)絡(luò)的局限性,使用MapReduce分布式編程模型對(duì)本文的算法進(jìn)行并行化,使得大規(guī)模網(wǎng)絡(luò)數(shù)據(jù)的社團(tuán)檢測(cè)得以實(shí)現(xiàn)。基于相似度的有向網(wǎng)絡(luò)社團(tuán)檢測(cè)算法利用相似度改進(jìn)CNM算法,使得算法與網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)相關(guān),并且利用方向信息指導(dǎo)相似度的計(jì)算,使得有向網(wǎng)絡(luò)社團(tuán)檢測(cè)得以實(shí)現(xiàn)。在單機(jī)和分布式環(huán)境下分別進(jìn)行實(shí)驗(yàn),結(jié)果表明本文的算法具有較高的準(zhǔn)確性,對(duì)大規(guī)模網(wǎng)絡(luò)數(shù)據(jù)的處理具有高效性。
[Abstract]:Community structure is a hot topic in the research of complex networks. In recent years, many community detection algorithms have been proposed, but most of the algorithms proposed at present are mainly suitable for community structure mining in undirected networks. For directed networks, these algorithms are often difficult to achieve better community detection results. In addition, because of the large amount of data, the traditional community detection algorithm can not meet the needs of computing efficiency and data storage. Therefore, it is of great significance to find a large-scale directed network community detection algorithm for community research. The main work of this paper is as follows: (1) the theoretical basis of complex network, the concept of community and Hadoop technology are introduced in detail, and some representative community detection algorithms are analyzed and their advantages and disadvantages are pointed out. The current situation and problems of community detection are deeply studied. (2) the similarity based directed network community detection algorithm is studied, which uses the direction information of the network to guide the node similarity calculation. Then the topological structure information of the directed network is transformed into the algebraic value, and then the similarity is used to improve the CNM algorithm. Combined with the advantages of CNM algorithm, the accuracy and applicability of the algorithm are improved. (3) aiming at the limitation of traditional community detection algorithm can not effectively deal with large-scale networks in a single computer environment, The algorithm is parallelized by MapReduce distributed programming model, which makes the community detection of large-scale network data possible. Similarity based directed network community detection algorithm using similarity to improve the CNM algorithm, make the algorithm related to the network topology, and use direction information to guide the similarity calculation, so that the directed network community detection can be realized. Experiments are carried out in single computer and distributed environment. The results show that the proposed algorithm has high accuracy and high efficiency for large-scale network data processing.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O157.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張海燕;梁循;周小平;;針對(duì)有向圖的局部擴(kuò)展的重疊社區(qū)發(fā)現(xiàn)算法[J];數(shù)據(jù)采集與處理;2015年03期

2 甘月松;陳秀宏;陳曉暉;;一種AP算法的改進(jìn):M-AP聚類算法[J];計(jì)算機(jī)科學(xué);2015年01期

3 孫怡帆;李賽;;基于相似度的微博社交網(wǎng)絡(luò)的社區(qū)發(fā)現(xiàn)方法[J];計(jì)算機(jī)研究與發(fā)展;2014年12期

4 李鋒;;一種有向網(wǎng)絡(luò)中社團(tuán)結(jié)構(gòu)劃分的啟發(fā)式算法[J];系統(tǒng)管理學(xué)報(bào);2014年04期

5 鄧波;張玉超;金松昌;林旺群;;基于MapReduce并行架構(gòu)的大數(shù)據(jù)社會(huì)網(wǎng)絡(luò)社團(tuán)挖掘方法[J];計(jì)算機(jī)研究與發(fā)展;2013年S2期

6 呂韶華;楊亮;林鴻飛;;基于SimRank的跨領(lǐng)域情感傾向性分析算法研究[J];中文信息學(xué)報(bào);2012年06期

7 駱志剛;丁凡;蔣曉舟;石金龍;;復(fù)雜網(wǎng)絡(luò)社團(tuán)發(fā)現(xiàn)算法研究新進(jìn)展[J];國防科技大學(xué)學(xué)報(bào);2011年01期

8 蔡曉妍;戴冠中;楊黎斌;;基于譜聚類的復(fù)雜網(wǎng)絡(luò)社團(tuán)發(fā)現(xiàn)算法[J];計(jì)算機(jī)科學(xué);2009年09期

9 楊博;劉大有;金弟;馬海賓;;復(fù)雜網(wǎng)絡(luò)聚類方法[J];軟件學(xué)報(bào);2009年01期

10 ;Local and global approaches of affinity propagation clustering for large scale data[J];Journal of Zhejiang University(Science A:An International Applied Physics & Engineering Journal);2008年10期

相關(guān)博士學(xué)位論文 前2條

1 張皓;復(fù)雜網(wǎng)絡(luò)的穩(wěn)定與控制研究[D];華中科技大學(xué);2007年

2 王林;復(fù)雜網(wǎng)絡(luò)的SCALE-FREE性、SCALE-FREE現(xiàn)象及其控制[D];西北工業(yè)大學(xué);2006年

，

本文編號(hào)：2116532