本文選題：復雜網(wǎng)絡(luò) + 數(shù)據(jù)挖掘��； 參考：《西安電子科技大學》2013年碩士論文

【摘要】：眾所周知，復雜網(wǎng)絡(luò)廣泛存在于自然、生物、工程和人類社會領(lǐng)域。深入研究復雜網(wǎng)絡(luò)可以揭示隱藏的大量復雜系統(tǒng)的共同規(guī)律�，F(xiàn)階段總的來講，復雜網(wǎng)絡(luò)進入了數(shù)學、物理學、生物學、計算科學、網(wǎng)絡(luò)科學等高強度的跨界混搭狀態(tài)。復雜網(wǎng)絡(luò)傳播模型的研究最初始于20世紀60年代，由于輿論的散布和病毒傳播、擴散類似，因此現(xiàn)有的輿論傳播模型大都借鑒了最早提出的傳染病模型。而發(fā)現(xiàn)核心節(jié)點是傳播控制中的重要手段，目前國內(nèi)外對于復雜網(wǎng)絡(luò)靜態(tài)數(shù)據(jù)分析工作較多，而對于傳播控制中動態(tài)分析的則較少。 在網(wǎng)絡(luò)演化模型生成機制的研究基礎(chǔ)上，許多學者提出了很多改進模型。網(wǎng)絡(luò)演化模型不僅可以捕捉網(wǎng)絡(luò)生成的動態(tài)性，，而且對實際網(wǎng)絡(luò)的設(shè)計合理性和結(jié)構(gòu)特征研究具有十分重要的意義。根據(jù)信息傳播中擴大和縮小效應(yīng)，找出信息傳播過程中主要關(guān)鍵點，可以很好地應(yīng)用在輿情控制、廣告效應(yīng)、病毒傳播控制等領(lǐng)域。 本文的主要工作是研究復雜網(wǎng)絡(luò)傳播學中重要節(jié)點的發(fā)現(xiàn)，內(nèi)容如下: 1.介紹了復雜網(wǎng)絡(luò)演化的研究歷史以及相關(guān)的基礎(chǔ)理論，總結(jié)出典型的傳播模型研究實現(xiàn)算法概況，并給出比較、分析，得出各個算法的適用對象及其范圍。 2．本文針對已有模型中社區(qū)發(fā)現(xiàn)算法的不足點，提出了新的改進算法。即，基于邊凝聚系數(shù)的簡單圖社區(qū)結(jié)構(gòu)發(fā)現(xiàn)的研究提出了改進的邊的凝聚算法。通過已有的兩個數(shù)據(jù)集的實驗數(shù)據(jù)證實由于邊的凝聚算法在去邊之后只需要重新計算局部的其他邊的凝聚系數(shù)，所以時間復雜度大大降低。且通過實驗對比分析得到了重要節(jié)點衡量的量化。 3.基于微博信息平臺下，ROST軟件和SPSS軟件進行分析，如用戶的話題相似度，相互轉(zhuǎn)發(fā)、評論、關(guān)注與被關(guān)注等的頻繁程度，做出聚類劃分，并找出“興趣圈子”，實現(xiàn)對復雜網(wǎng)絡(luò)的動態(tài)數(shù)據(jù)分析。利用重要節(jié)點衡量標準分析得出實驗結(jié)果與實際情況相比較具有很強的吻合性。
[Abstract]:As we all know, complex networks exist widely in the fields of nature, biology, engineering and human society.A deep study of complex networks can reveal the common laws of a large number of hidden complex systems.At the present stage, complex networks have entered high intensity cross boundary mixing states, such as mathematics, physics, biology, computational science, network science and so on.The study of complex network communication model started in 1960s. Because the spread of public opinion and virus spread is similar, most of the existing public opinion communication models draw lessons from the first proposed infectious disease model.The discovery of core nodes is an important means of propagation control. At present, there are more work on static data analysis of complex networks at home and abroad, but less on dynamic analysis in propagation control.On the basis of the research on the generation mechanism of network evolution model, many scholars have proposed many improved models.The network evolution model can not only capture the dynamics of network generation, but also be of great significance to the study of the design rationality and structural characteristics of the actual network.According to the expanding and shrinking effect of information dissemination, the main key points in the process of information dissemination can be found out, which can be well applied in the fields of public opinion control, advertising effect, virus transmission control and so on.The main work of this paper is to study the discovery of important nodes in complex network communication. The contents are as follows:1.This paper introduces the history of complex network evolution and related basic theories, summarizes the general situation of typical propagation model research and realization algorithms, and gives the comparison and analysis, and obtains the applicable object and scope of each algorithm.2.In this paper, a new improved algorithm is proposed to overcome the shortcomings of the community discovery algorithm in the existing models.That is, an improved edge aggregation algorithm is proposed for community structure discovery of simple graphs based on edge cohesion coefficient.The experimental data from two existing datasets show that the time complexity is greatly reduced because the edge aggregation algorithm only needs to recalculate the local coacervation coefficients of other edges after edge removal.The quantization of the important nodes is obtained through the comparative analysis of experiments.3.Based on Weibo information platform and SPSS software analysis, such as users' topic similarity, mutual forwarding, comments, attention and attention frequency, make clustering division, and find out "interest circle".The dynamic data analysis of complex network is realized.It is found that the experimental results are in good agreement with the actual situation by using the measurement standard analysis of important nodes.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：O157.5

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