發(fā)布時間：2018-07-12 08:13

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

【摘要】：社團結(jié)構(gòu)是復雜網(wǎng)絡研究的熱點,近些年來提出了許多社團檢測算法,但目前提出的大多數(shù)算法主要適用于無向網(wǎng)絡的社團結(jié)構(gòu)挖掘,對于有向網(wǎng)絡這些算法往往難以取得較好的社團檢測效果。另外,大規(guī)模數(shù)據(jù)量的產(chǎn)生使得傳統(tǒng)的社團檢測算法不能滿足計算效率以及數(shù)據(jù)存儲等方面的需求。因此,尋找大規(guī)模有向網(wǎng)絡的社團檢測算法對于社團的研究具有重要的意義。本文主要工作如下:(1)對復雜網(wǎng)絡的理論基礎(chǔ)、社團的概念以及Hadoop技術(shù)進行了詳細的介紹,分析了一些具有代表性的社團檢測算法并指出其各自的優(yōu)缺點;深入研究了社團檢測的研究現(xiàn)狀和所面臨的問題。(2)研究基于相似度的有向網(wǎng)絡社團檢測算法,該算法利用網(wǎng)絡的方向信息指導節(jié)點相似度的計算,從而把有向網(wǎng)絡的拓撲結(jié)構(gòu)信息轉(zhuǎn)化為代數(shù)值,然后利用相似度改進CNM算法,結(jié)合CNM算法本身的優(yōu)點提高了算法的準確性和適用性。(3)針對傳統(tǒng)社團檢測算法在單機環(huán)境下不能有效處理大規(guī)模網(wǎng)絡的局限性,使用MapReduce分布式編程模型對本文的算法進行并行化,使得大規(guī)模網(wǎng)絡數(shù)據(jù)的社團檢測得以實現(xiàn)�；谙嗨贫鹊挠邢蚓W(wǎng)絡社團檢測算法利用相似度改進CNM算法,使得算法與網(wǎng)絡的拓撲結(jié)構(gòu)相關(guān),并且利用方向信息指導相似度的計算,使得有向網(wǎng)絡社團檢測得以實現(xiàn)。在單機和分布式環(huán)境下分別進行實驗,結(jié)果表明本文的算法具有較高的準確性,對大規(guī)模網(wǎng)絡數(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.
【學位授予單位】：西安理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：O157.5

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本文編號：2116532