【摘要】：近幾年隨著互聯(lián)網(wǎng)“爆炸性”的增長,人與人就交流得更加密切,社會網(wǎng)絡(luò)也隨之飛速發(fā)展,而社會網(wǎng)絡(luò)又是由個人和組織以及他們之間的聯(lián)系(交互)構(gòu)成的集合,而現(xiàn)在研究的熱點問題也逐漸變成了大規(guī)模的社會網(wǎng)絡(luò)發(fā)現(xiàn)問題。在現(xiàn)實生活中,網(wǎng)絡(luò)是與我們息息相關(guān)的,社會的政治經(jīng)濟活動,朋友之間的日常交際,科學(xué)家的合作等這些現(xiàn)象可以從社會網(wǎng)絡(luò)的角度研究。通過研究這樣的網(wǎng)絡(luò)結(jié)構(gòu),我們可以用于分析信息的傳播規(guī)律,把有著共同興趣愛好的人們連接在一起,為人們提供一個更完善的交流平臺和工具,或阻止不良信息的傳播。本文就是基于人與人之間的交互行為,提出交互度用來度量網(wǎng)絡(luò)成員之間的交互,并基于交互度提出了一個大規(guī)模社會網(wǎng)絡(luò)的社區(qū)發(fā)現(xiàn)算法。本文的主要工作概括如下： (1)提出分層次大規(guī)模網(wǎng)絡(luò)社區(qū)發(fā)現(xiàn)的方法,大規(guī)模網(wǎng)絡(luò)的核心問題是網(wǎng)絡(luò)規(guī)模過大,針對這個問題,本文利用分層聚類的思想提出了大規(guī)模社會網(wǎng)絡(luò)的社區(qū)發(fā)現(xiàn)算法。首先,將大規(guī)模網(wǎng)絡(luò)先進行預(yù)處理,劃分為局部小的網(wǎng)絡(luò),并在小規(guī)模網(wǎng)絡(luò)中進行社區(qū)發(fā)現(xiàn)。隨后,將小規(guī)模網(wǎng)絡(luò)中發(fā)現(xiàn)的社區(qū)看作一個點,在后處理階段重構(gòu)網(wǎng)絡(luò),基于重構(gòu)網(wǎng)絡(luò)進行全局社區(qū)發(fā)現(xiàn)。 (2)基于交互度的計算,完成了交互度量從局部向全局的傳遞。大規(guī)模網(wǎng)絡(luò)劃分以后,通過交互度的計算,成員之間和社區(qū)之間的交互關(guān)系度量能夠從局部向全局傳遞,保持度量的一致性,從而保證社區(qū)發(fā)現(xiàn)結(jié)果的準(zhǔn)確性。 (3)算法被應(yīng)用于真實社會網(wǎng)絡(luò)和人工模擬的網(wǎng)絡(luò)上,分別對本文算法的準(zhǔn)確度和效率兩個方面進行了測試,說明了算法有效性和效率。 綜上所述,本文的工作是面對社會網(wǎng)絡(luò)分析的背景下,提出基于交互度的大規(guī)模社會網(wǎng)絡(luò)的社區(qū)發(fā)現(xiàn)算法,并且能夠得到一個較為精確的社區(qū)劃分。
[Abstract]:In recent years, with the "explosive" growth of the Internet, people have been communicating more closely with each other, and social networks have also developed rapidly, and social networks are a collection of individuals and organizations and their connections (interactions). And now the hot issue of research has gradually become a large-scale social network discovery problem. In real life, the network is closely related to us. The social political and economic activities, the daily communication between friends, the cooperation of scientists and so on can be studied from the perspective of social network. By studying such a network structure, we can be used to analyze the law of information dissemination, connect people with common interests, provide people with a more perfect communication platform and tools, or prevent the dissemination of bad information. In this paper, based on the interaction behavior between people, the interaction degree is proposed to measure the interaction between network members, and a community discovery algorithm for large-scale social networks is proposed based on the interaction degree. The main work of this paper is summarized as follows: (1) the method of hierarchical large-scale network community discovery is proposed. The core problem of large-scale network is that the network scale is too large. In this paper, a community discovery algorithm for large-scale social networks is proposed by using the idea of hierarchical clustering. First of all, the large-scale network is preprocessed and divided into locally small networks, and community discovery is carried out in small-scale networks. Then, the community found in the small-scale network is regarded as a point, and the network is reconstructed in the post-processing stage, and the global community discovery is carried out based on the reconstructed network. (2) based on the calculation of interaction degree, the transfer of interaction measurement from local to global is completed. After large-scale network partition, through the calculation of interaction degree, the measurement of interaction between members and communities can be transferred from local to global, and the consistency of measurement can be maintained, so as to ensure the accuracy of community discovery results. (3) the algorithm is applied to real social network and artificial simulated network, and the accuracy and efficiency of the algorithm are tested respectively, and the effectiveness and efficiency of the algorithm are illustrated. To sum up, the work of this paper is to propose a large-scale social network community discovery algorithm based on interaction under the background of social network analysis, and can get a more accurate community division.
【學(xué)位授予單位】：云南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：O157.5

【參考文獻】

相關(guān)期刊論文 前2條

1 王剛;鐘國祥;;基于信息熵的社區(qū)發(fā)現(xiàn)算法研究[J];計算機科學(xué);2011年02期

2 王林,戴冠中;復(fù)雜網(wǎng)絡(luò)中的社區(qū)發(fā)現(xiàn)——理論與應(yīng)用[J];科技導(dǎo)報;2005年08期

，

本文編號：2474022