發(fā)布時(shí)間：2018-02-01 19:51

  本文關(guān)鍵詞： 社交網(wǎng)絡(luò) 節(jié)點(diǎn)中心性 疊瓦算法 社區(qū)挖掘 環(huán)境節(jié)點(diǎn)　出處：《西安電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：在Web 1.0時(shí)代,信息是由專業(yè)的網(wǎng)站發(fā)布,用戶只能瀏覽、下載,不能參與信息的產(chǎn)生和傳播。進(jìn)入21世紀(jì)后,Web2.0逐漸興起,并且發(fā)展勢(shì)頭迅猛,這種信息產(chǎn)生和傳播模式也逐步發(fā)生了轉(zhuǎn)變。用戶由原來(lái)的只能瀏覽、下載轉(zhuǎn)變成既可以產(chǎn)生信息,又可以傳播信息,當(dāng)然更可以閱讀。用戶也從原來(lái)的信息消費(fèi)者一種角色轉(zhuǎn)變成了消費(fèi)者和生產(chǎn)者的兩種角色。于是,人們將學(xué)習(xí)和生活以及工作的很大一部分時(shí)間轉(zhuǎn)移到了網(wǎng)上,尤其是社交網(wǎng)絡(luò)上。于是社交網(wǎng)絡(luò)成了人們必不可少的一部分。在社交網(wǎng)絡(luò)上,每天都有海量的數(shù)據(jù)產(chǎn)生。通過(guò)分析這些海量數(shù)據(jù),可以得到人們的興趣偏好、地域分布、生活狀態(tài)以及一些事件的發(fā)展動(dòng)態(tài)等等的潛在信息。這些信息有著特別巨大的社會(huì)價(jià)值和商業(yè)價(jià)值。政府可以參考相關(guān)信息進(jìn)行政策調(diào)整,公司可以根據(jù)分析出的結(jié)果針對(duì)性的進(jìn)行產(chǎn)品開(kāi)發(fā)以及調(diào)整公司戰(zhàn)略等等。因此,分析研究社交網(wǎng)絡(luò)有著極大的理論意義和實(shí)際意義。本文以豆瓣網(wǎng)為社交網(wǎng)絡(luò)實(shí)驗(yàn)數(shù)據(jù),研究社交網(wǎng)絡(luò)分析的關(guān)鍵技術(shù),包括驗(yàn)證社交年網(wǎng)絡(luò)的基本結(jié)構(gòu)特性、分析社交網(wǎng)絡(luò)節(jié)點(diǎn)性質(zhì)以及社區(qū)發(fā)現(xiàn),因此本文的研究工作可以分為三個(gè)部分。首先,本文分析和驗(yàn)證了社交網(wǎng)絡(luò)基本結(jié)構(gòu)特性的正確性,包括無(wú)標(biāo)度性、小世界性和社區(qū)結(jié)構(gòu)。其次,本文從“度中心性”和“緊密度中心性”兩個(gè)方面對(duì)社交網(wǎng)絡(luò)中的節(jié)點(diǎn)進(jìn)行了研究。最后,本文深入分析研究了“遞歸疊瓦算法”這種社區(qū)發(fā)現(xiàn)算法;通過(guò)實(shí)驗(yàn)發(fā)現(xiàn)其缺陷,并分析了缺陷成因且進(jìn)行了實(shí)驗(yàn)驗(yàn)證;在此其基礎(chǔ)上,本文提出了“添加環(huán)境節(jié)點(diǎn)”的改進(jìn)方法并用實(shí)驗(yàn)驗(yàn)證其正確性。在最后一部分中,除了社區(qū)發(fā)現(xiàn)的研究之外,通過(guò)對(duì)比分析節(jié)點(diǎn)時(shí)得到的“中心節(jié)點(diǎn)集”與社區(qū)挖掘得到的“社區(qū)節(jié)點(diǎn)集”,本文發(fā)現(xiàn)了“中心節(jié)點(diǎn)不一定在社區(qū)結(jié)構(gòu)內(nèi)”的現(xiàn)象,并對(duì)其形成原因進(jìn)行了分析和實(shí)驗(yàn)驗(yàn)證;以此為基礎(chǔ),本文提出了社交網(wǎng)絡(luò)節(jié)點(diǎn)分類方案,并為社交網(wǎng)絡(luò)上的產(chǎn)品推廣提出了建議。
[Abstract]:In the era of Web 1. 0, information is published by professional websites, users can only browse, download, can not participate in the generation and dissemination of information. And the rapid development momentum, this information generation and dissemination mode has gradually changed. Users can only browse from the original, download to not only produce information, but also can spread information. Of course, it is more readable. Users have also changed from one role of information consumer to two roles of consumer and producer. People move much of their study and life and work to the Internet, especially on social networks. So social networks become an essential part of people. On social networks. Through analyzing these massive data, we can get people's interest preference and regional distribution. Potential information about the state of life and the development of some events. This information has particularly great social and commercial value. The government can refer to the relevant information for policy adjustment. Companies can be based on the results of the analysis of targeted product development and adjust the company strategy and so on. It is of great theoretical and practical significance to analyze and study social networks. In this paper, we take Douban net as the experimental data of social networks, and study the key technologies of social network analysis, including verifying the basic structural characteristics of social networks. Analysis of the nature of social network nodes and community discovery, so this study can be divided into three parts. First, this paper analyzes and verifies the correctness of the basic structural characteristics of social networks, including scale-free. Secondly, this paper studies the nodes in social networks from the aspects of "degree centrality" and "compactness centrality". In this paper, the community discovery algorithm named "recursive imprint algorithm" is deeply analyzed and studied. The defects are found through experiments, and the causes of the defects are analyzed and verified by experiments. On this basis, this paper proposes an improved method of "adding environment nodes" and verifies its correctness by experiments. In the last part, in addition to the community discovery research. By comparing and analyzing the "central node set" and the "community node set" obtained by community mining, this paper finds that "the central node is not necessarily within the community structure" phenomenon. The causes of its formation are analyzed and verified by experiments. Based on this, this paper proposes a classification scheme of social network nodes, and gives some suggestions for product promotion on social networks.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.09

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