本文選題：復(fù)雜網(wǎng)絡(luò) + 社區(qū)挖掘�。� 參考：《中國礦業(yè)大學(xué)》2015年碩士論文

【摘要】：研究表明,復(fù)雜網(wǎng)絡(luò)普遍存在社區(qū)結(jié)構(gòu),社區(qū)內(nèi)部節(jié)點(diǎn)之間具有更加密切的聯(lián)系。社區(qū)挖掘的目的是從復(fù)雜網(wǎng)絡(luò)中挖掘出社區(qū)結(jié)構(gòu),進(jìn)一步認(rèn)識網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)和功能,探索網(wǎng)絡(luò)的動力學(xué)特性及其演化機(jī)制。社區(qū)挖掘研究具有十分重要的理論意義和實(shí)際應(yīng)用價值。本課題主要針對基于標(biāo)簽傳播的社區(qū)挖掘算法準(zhǔn)確率低、穩(wěn)定性差的缺點(diǎn),給出兩種改進(jìn)標(biāo)簽傳播算法,有效利用網(wǎng)絡(luò)節(jié)點(diǎn)中心性在社區(qū)挖掘中的作用,降低因平等對待每個節(jié)點(diǎn),并按照隨機(jī)順序更新標(biāo)簽造成的社區(qū)劃分錯誤和不穩(wěn)定性。主要工作包括:第一,給出一種基于局部核心節(jié)點(diǎn)的標(biāo)簽傳播算法,該算法利用節(jié)點(diǎn)度中心性定義局部核心節(jié)點(diǎn),分別給這些節(jié)點(diǎn)及其鄰居分配相同的標(biāo)簽,然后進(jìn)行標(biāo)簽更新,實(shí)驗(yàn)結(jié)果表明該算法能有效提高社區(qū)挖掘質(zhì)量及算法穩(wěn)定性,同時維持標(biāo)簽傳播算法的近線性時間復(fù)雜度;第二,給出節(jié)點(diǎn)的Leader Rank中心性和中心節(jié)點(diǎn)的概念,并給出一種基于Leader Rank中心節(jié)點(diǎn)擴(kuò)展的標(biāo)簽傳播算法,該算法首先找出局部Leader Rank中心節(jié)點(diǎn),并以它們?yōu)闃?biāo)簽傳播源,以節(jié)點(diǎn)Leader Rank中心性為標(biāo)簽更新優(yōu)先度,采用新的更新策略進(jìn)行標(biāo)簽傳播,從而挖掘出社區(qū)結(jié)構(gòu),實(shí)驗(yàn)結(jié)果表明,該算法相較于其他幾種代表性算法的社區(qū)挖掘準(zhǔn)確率及穩(wěn)定性都得到大大提升。
[Abstract]:The research shows that there is a community structure in complex networks, and there is a closer relationship between the nodes within the community. The purpose of community mining is to excavate the community structure from complex networks, to further understand the topology and function of networks, and to explore the dynamic characteristics and evolution mechanism of networks. Community mining research has very important theoretical significance and practical application value. Aiming at the shortcomings of low accuracy and poor stability of the community mining algorithm based on label propagation, two improved label propagation algorithms are presented to effectively utilize the role of network node centrality in community mining. Reduce community partitioning errors and instability caused by equal treatment of each node and updating labels in random order. The main contributions are as follows: first, a label propagation algorithm based on local core nodes is proposed. The algorithm defines local core nodes by using node centrality, and assigns the same labels to these nodes and their neighbors, respectively. The experimental results show that the algorithm can effectively improve the quality of community mining and stability of the algorithm, while maintaining the near linear time complexity of the label propagation algorithm. Second, In this paper, the concept of Leader Rank centrality and central node is given, and a label propagation algorithm based on leader Rank central node extension is given. Firstly, the local leader Rank central node is found and used as label propagation source. The node Leader Rank centrality is regarded as the priority of tag updating, and the new updating strategy is adopted to propagate the tag, thus mining out the community structure. The experimental results show that, Compared with other typical algorithms, the accuracy and stability of community mining are greatly improved.
【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：O157.5;TP301.6

【共引文獻(xiàn)】

相關(guān)博士學(xué)位論文 前5條

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4 馬小科;復(fù)雜網(wǎng)絡(luò)社團(tuán)結(jié)構(gòu)模型與算法及其在生物網(wǎng)絡(luò)中的應(yīng)用[D];西安電子科技大學(xué);2014年

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相關(guān)碩士學(xué)位論文 前3條

1 萬果鋒;基于郵件系統(tǒng)的社團(tuán)挖掘研究[D];大連交通大學(xué);2010年

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3 彭前進(jìn);群落網(wǎng)絡(luò)的同步特點(diǎn)研究[D];廣西師范大學(xué);2013年

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本文編號：2047828