本文關鍵詞： 社區(qū)發(fā)現 部分先驗知識 標簽傳播 局部回路 數據集　出處：《天津科技大學》2016年碩士論文　論文類型：學位論文

【摘要】：隨著DT(Data Technology)時代的到來,數據的價值在各行各業(yè)中越來越得到廣泛重視。如何從紛繁復雜的數據中發(fā)掘去一些有價值的信息來指導和改善我們的工作和生活具有重要的意義。社區(qū)發(fā)現是復雜網絡研究領域一個重要的研究方向,可以從紛繁復雜的網絡數據中尋找一些潛在的社區(qū)結構,發(fā)現隱藏在網絡海量數據中的知識和潛藏在一般現象下的規(guī)律,進而為人們提供個性化、科學化的服務,幫助人們作出更有效的決策。本文通過對標簽傳播算法的研究,結合社區(qū)發(fā)現過程中的先驗知識,提出了一種基于局部回路的標簽傳播社區(qū)發(fā)現算法,并通過實驗對算法進行了驗證。本文的研究工作主要包括以下兩個方面:(1)提出了一種基于局部回路的標簽傳播社區(qū)發(fā)現算法。首先,綜述了社區(qū)發(fā)現算法,并重點分析了標簽傳播算法及其存在的問題。其次,根據社區(qū)發(fā)現過程中節(jié)點間存在的先驗知識,提出了基于局部回路的標簽傳播改進算法,即標簽傳播過程中,當存在多個最大標簽值時,采用最短局部回路選擇策略代替隨機選擇,從而有效抑制標簽在社區(qū)間傳播,提高算法的準確度,并用簡單示例從理論角度驗證了算法的可行性。最后,為了驗證改進算法的有效性,本文選擇了兩種類型的數據集,分別采用經典真實數據集、人工生成基準數據集,并以模塊度和NMI為評價標準,用對比的方法對本文提出的改進算法進行驗證。實驗結果表明基于局部回路的標簽傳播算法可以取得更好的劃分效果。(2)實驗驗證。選取代表性的微博真實網絡為實驗數據集,通過預處理剔除特殊點,再將改進算法應用到真實的微博網絡的劃分中,驗證改進的算法在真實網絡中也能取到較好的劃分結果。
[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.
【學位授予單位】：天津科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP301.6

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