本文關鍵詞：基于隨機塊模型的大規(guī)模社會網(wǎng)絡中觀關鍵結(jié)構(gòu)研究　出處：《太原理工大學》2015年碩士論文　論文類型：學位論文

  更多相關文章： 大規(guī)模社會網(wǎng)絡 關鍵結(jié)構(gòu) 隨機塊模型 社區(qū)結(jié)構(gòu) 結(jié)構(gòu)洞節(jié)點

【摘要】：隨著互聯(lián)網(wǎng)的不斷發(fā)展，智能手機、平板電腦等智能終端在人類生活中的普及以及移動網(wǎng)絡帶寬的不斷提高，使得微信、微博等社交媒體已經(jīng)漸漸成為了人類生活中不可或缺的部分。人們在真實世界中的互動與聯(lián)系，也不斷的向互聯(lián)網(wǎng)上拓展，使得我們的社會也被社會化了。作為一種社會學理論、信息處理及電子商務等多個學科的交叉熱點，大規(guī)模社會網(wǎng)絡應運而生。大規(guī)模社會網(wǎng)絡中，每天產(chǎn)生的海量信息數(shù)據(jù)具有很高的價值。對這些數(shù)據(jù)的發(fā)掘和分析成為研究大規(guī)模社會網(wǎng)絡的重要途徑。通過對大規(guī)模社會網(wǎng)絡數(shù)據(jù)的分析和有效信息的發(fā)掘，能夠更好的理解大規(guī)模社會網(wǎng)絡，為進一步的輿情監(jiān)控、電子商務個性化推薦等實際應用的研究提供了理論基礎。 目前，社會網(wǎng)絡的研究已經(jīng)逐漸從對中小規(guī)模的在線社會網(wǎng)絡結(jié)構(gòu)研究，進入到了大規(guī)模社會網(wǎng)絡結(jié)構(gòu)的研究中。同時，研究的目標也從發(fā)現(xiàn)指定單一結(jié)構(gòu)方面向多結(jié)構(gòu)發(fā)現(xiàn)方向轉(zhuǎn)變。大規(guī)模社會網(wǎng)絡結(jié)構(gòu)多樣化研究已經(jīng)成為了當前社會網(wǎng)絡結(jié)構(gòu)研究的熱點和重點。然而，現(xiàn)有的研究，往往從特定的結(jié)構(gòu)研究出發(fā)，沒有考慮到多種結(jié)構(gòu)相互配合，能夠更好的反應出大規(guī)模社會網(wǎng)絡結(jié)構(gòu)的特點。基于以上的考慮，本文針對大規(guī)模社會網(wǎng)絡的結(jié)構(gòu)問題進行了研究，提出了新的概念及相關定義與實現(xiàn)算法。本文的貢獻主要有以下幾方面： 首先，在大規(guī)模社會網(wǎng)絡結(jié)構(gòu)研究的傳統(tǒng)定義上，，充分考慮了多種結(jié)構(gòu)在大規(guī)模社會網(wǎng)絡結(jié)構(gòu)分析的重要性，提出了關鍵結(jié)構(gòu)概念。同時分別給出了關鍵結(jié)構(gòu)概念的一般化定義。并依照關鍵結(jié)構(gòu)的定義，說明了關鍵結(jié)構(gòu)的發(fā)現(xiàn)過程，提出了關鍵結(jié)構(gòu)的評價標準。 其次，給出了結(jié)構(gòu)洞節(jié)點的描述性定義，分別介紹了用于發(fā)現(xiàn)關鍵結(jié)構(gòu)的隨機塊模型SBM(Stochastic Block Model)及結(jié)構(gòu)洞節(jié)點發(fā)現(xiàn)方法SHSD(Structural Hole Spanners Detection)。 最后，使用名為BC的博客數(shù)據(jù)集和名為MB的微博數(shù)據(jù)集為實驗數(shù)據(jù)集，進行相關實驗。實驗結(jié)果表明，隨機塊模型能夠發(fā)現(xiàn)社會網(wǎng)絡中的社區(qū)結(jié)構(gòu)，且在分類結(jié)果上使用結(jié)構(gòu)洞發(fā)現(xiàn)方法SHSD能夠發(fā)現(xiàn)網(wǎng)絡中的結(jié)構(gòu)洞節(jié)點。通過與原始輸入網(wǎng)絡數(shù)據(jù)圖進行比較，實驗發(fā)現(xiàn)的關鍵結(jié)構(gòu)能夠較為全面的描述整個社會網(wǎng)絡的結(jié)構(gòu)特征。
[Abstract]:With the continuous development of the Internet, smart phones, tablets and other intelligent terminals in human life, as well as mobile network bandwidth continues to improve, making WeChat. Social media such as Weibo have become an integral part of human life. People's interactions and connections in the real world are expanding to the Internet. As a kind of sociological theory, information processing and electronic commerce and so on, the large-scale social network emerges as the times require. In the large-scale social network, the large-scale social network emerges as the times require. The massive information data produced every day is of great value. The discovery and analysis of these data has become an important way to study large-scale social network. Through the analysis of large-scale social network data and effective information mining. . It can better understand the large-scale social network, which provides a theoretical basis for the further research of public opinion monitoring, e-commerce personalized recommendation and other practical applications. At present, the research of social network has gradually moved from the research of small and medium scale online social network structure to the research of large-scale social network structure. At the same time. The goal of the study is also changed from finding a single structure to a multi-structure discovery. The research of large-scale social network structure diversification has become the focus and focus of the current social network research. Existing studies, often from the specific structure of the study, do not take into account a variety of structures to cooperate with each other, can better reflect the characteristics of large-scale social network structure. Based on the above considerations. In this paper, the structure of large-scale social networks is studied, and a new concept, related definitions and implementation algorithms are proposed. The contributions of this paper are as follows: Firstly, in the traditional definition of large-scale social network structure research, the importance of various structures in large-scale social network structure analysis is fully considered. In this paper, the concept of key structure is put forward, and the general definition of the concept of key structure is given. According to the definition of key structure, the discovery process of key structure is explained, and the evaluation criteria of key structure are put forward. Secondly, the descriptive definition of the structure hole node is given. The random block model (SBM(Stochastic Block Model) used to discover key structures and the method of structural hole node discovery (SHSD) are introduced respectively. Structural Hole Spanners detection. Finally, using the blog data set named BC and Weibo dataset named MB as the experimental data set, the experiment results show that the random block model can find the community structure in the social network. The structural hole nodes in the network can be found by using the structure hole discovery method (SHSD) in the classification results, and compared with the original input network data graph. The key structure found in the experiment can describe the structural characteristics of the whole social network comprehensively.
【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP393.092;TP311.13

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本文編號：1399659