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研究生: 陳明謙
Ming-chien Chen
論文名稱: 社群影響力分析於社群網路中
Community Based Influence Analysis in Social Network
指導教授: 蔡孟峰
口試委員:
學位類別: 碩士
Master
系所名稱: 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
論文出版年: 2013
畢業學年度: 101
語文別: 中文
論文頁數: 54
中文關鍵詞: 社群網路分析HITS演算法訊息傳播分析社群影響力
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  • 近年來,社群網站的興起,例如:Facebook之類的網站受到人們廣泛的使用,這些網站擁有大量使用者的相關資訊。許多研究學者紛紛開始研究如何有效分析大量的社群網路資料。訊息傳播與影響力分析是社群網路研究中被關注的問題之一。隨著許多社群網站提供創立社團功能後,越來越使用者包括企業團體或是各式社會組織紛紛成立社團,希望透過社團快速分享資訊給有興趣使用者。然而近年來研究通常都只著重於使用者之間訊息傳播和利用分群後的社群,探討使用者在社群內與社群間訊息傳播,來評估個別使用者之訊息傳播影響力。以上研究未考慮到社群與社群之間訊息傳播,在此我們探討社群之間互相影響分析,可提供行銷人員不同層面的行銷方式。本論文提供社群與社群之間訊息傳播分析,探討社群網路中社群影響力,找出社群間互相影響關係,共分為兩部分:第一部分,根據社群網站提供的社群資訊,以及使用者之間的訊息傳播與影響關係,定義出兩個社群之間互相影響程度;第二部分,利用網路連結分析演算法Randomized HITS algorithm評估社群在整個社群網路中的影響力。
    本論文採用真實社群資訊,以社群層面探討在社群網路中影響力,讓專家得以分析社群行為與特性,有利於企業做網路口碑行銷,社會團體的活動宣傳。


    In recent years, the rise of social websites, such as: Facebook, like the website has been widely used, these websites have a lot of information about the user. Many researchers have begun to study how to effectively analyze large amounts of social network data. Information diffusion and influence of social network analysis is one of the concerns being. However, recent studies usually only focus on the behavior of information diffusion between users, instead of considering the behavior of information diffusion between different social communities Therefore, this paper provides information diffusion between communities and influence of community in the social networks, find out the mutual influence between community relations. This research is composed of two parts: the first part, according to the community website provides Community information and messages between users spread and influence relationships define the degree of mutual influence between the two communities; the second part, the use of Randomized HITS algorithm methods to measure the influence rank of communities in social network.

    摘要 i Abstract ii 目錄 iii 圖目錄 iv 一、緒論 1 1.1 背景介紹 1 1.2 論文章節介紹 6 二.文獻探討 7 2.1資料探勘 7 2.2訊息傳播與影響力分析 7 三、背景介紹 10 3.1 Independent Cascade Model 10 3.2 Greedy 演算法 11 3.3 HITS algorithm 11 3.4 Randomized HITS 14 四、研究方法 16 4.1任兩個社群之間互相影響計算 16 4.2建構Community Influence Graph 24 4.3 Randomized HITS評估社群的影響力 26 五、實驗 29 5.1 實驗資料 29 5.2 實驗設定 29 5.3 實驗結果 30 六、結論 46 參考文獻 47

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