本文關(guān)鍵詞： 社區(qū)發(fā)現(xiàn) 部分先驗(yàn)知識(shí) 標(biāo)簽傳播 局部回路 數(shù)據(jù)集　出處：《天津科技大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著DT(Data Technology)時(shí)代的到來(lái),數(shù)據(jù)的價(jià)值在各行各業(yè)中越來(lái)越得到廣泛重視。如何從紛繁復(fù)雜的數(shù)據(jù)中發(fā)掘去一些有價(jià)值的信息來(lái)指導(dǎo)和改善我們的工作和生活具有重要的意義。社區(qū)發(fā)現(xiàn)是復(fù)雜網(wǎng)絡(luò)研究領(lǐng)域一個(gè)重要的研究方向,可以從紛繁復(fù)雜的網(wǎng)絡(luò)數(shù)據(jù)中尋找一些潛在的社區(qū)結(jié)構(gòu),發(fā)現(xiàn)隱藏在網(wǎng)絡(luò)海量數(shù)據(jù)中的知識(shí)和潛藏在一般現(xiàn)象下的規(guī)律,進(jìn)而為人們提供個(gè)性化、科學(xué)化的服務(wù),幫助人們作出更有效的決策。本文通過(guò)對(duì)標(biāo)簽傳播算法的研究,結(jié)合社區(qū)發(fā)現(xiàn)過(guò)程中的先驗(yàn)知識(shí),提出了一種基于局部回路的標(biāo)簽傳播社區(qū)發(fā)現(xiàn)算法,并通過(guò)實(shí)驗(yàn)對(duì)算法進(jìn)行了驗(yàn)證。本文的研究工作主要包括以下兩個(gè)方面:(1)提出了一種基于局部回路的標(biāo)簽傳播社區(qū)發(fā)現(xiàn)算法。首先,綜述了社區(qū)發(fā)現(xiàn)算法,并重點(diǎn)分析了標(biāo)簽傳播算法及其存在的問(wèn)題。其次,根據(jù)社區(qū)發(fā)現(xiàn)過(guò)程中節(jié)點(diǎn)間存在的先驗(yàn)知識(shí),提出了基于局部回路的標(biāo)簽傳播改進(jìn)算法,即標(biāo)簽傳播過(guò)程中,當(dāng)存在多個(gè)最大標(biāo)簽值時(shí),采用最短局部回路選擇策略代替隨機(jī)選擇,從而有效抑制標(biāo)簽在社區(qū)間傳播,提高算法的準(zhǔn)確度,并用簡(jiǎn)單示例從理論角度驗(yàn)證了算法的可行性。最后,為了驗(yàn)證改進(jìn)算法的有效性,本文選擇了兩種類型的數(shù)據(jù)集,分別采用經(jīng)典真實(shí)數(shù)據(jù)集、人工生成基準(zhǔn)數(shù)據(jù)集,并以模塊度和NMI為評(píng)價(jià)標(biāo)準(zhǔn),用對(duì)比的方法對(duì)本文提出的改進(jìn)算法進(jìn)行驗(yàn)證。實(shí)驗(yàn)結(jié)果表明基于局部回路的標(biāo)簽傳播算法可以取得更好的劃分效果。(2)實(shí)驗(yàn)驗(yàn)證。選取代表性的微博真實(shí)網(wǎng)絡(luò)為實(shí)驗(yàn)數(shù)據(jù)集,通過(guò)預(yù)處理剔除特殊點(diǎn),再將改進(jìn)算法應(yīng)用到真實(shí)的微博網(wǎng)絡(luò)的劃分中,驗(yàn)證改進(jìn)的算法在真實(shí)網(wǎng)絡(luò)中也能取到較好的劃分結(jié)果。
[Abstract]:With the advent of the DT(Data Technology era. The value of data is getting more and more attention in a variety of industries. How to extract valuable information from complex data to guide and improve our work and life is important. Community discovery is. The research field of complex network is an important research direction. We can find some potential community structure from the complicated network data, find the knowledge hidden in the massive network data and the law hidden under the general phenomenon, and then provide individuation for people. Scientific service helps people to make more effective decision. This paper combines the prior knowledge in the process of community discovery through the research of label propagation algorithm. A local loop based label propagation community discovery algorithm is proposed. The research work of this paper mainly includes the following two aspects: 1) A label propagation community discovery algorithm based on local loop is proposed. First of all. This paper summarizes the community discovery algorithm, and analyzes the label propagation algorithm and its existing problems. Secondly, according to the prior knowledge among the nodes in the process of community discovery. An improved label propagation algorithm based on local loop is proposed. In the process of label propagation, when there are multiple maximum label values, the shortest local loop selection strategy is used instead of random selection. In order to effectively suppress the spread of labels in the community, improve the accuracy of the algorithm, and a simple example from the theoretical point of view to verify the feasibility of the algorithm. Finally, in order to verify the effectiveness of the improved algorithm. In this paper, we choose two types of data sets, using classical real data sets, artificial generation of benchmark data sets, and the modular degree and NMI as the evaluation criteria. The experimental results show that the label propagation algorithm based on local loop can achieve better partition effect. Experimental verification. The representative Weibo real network is selected as the experimental data set. The improved algorithm is applied to the partition of real Weibo network by eliminating the special points by preprocessing, and it is verified that the improved algorithm can also obtain better partition results in real network.
【學(xué)位授予單位】：天津科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP301.6

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