博碩士論文 101423050 詳細資訊




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姓名 黃學惇(Hsueh-tun Huang)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 利用核心節點及區域社群以改善社群探勘之凝聚法技術的方法
(A Novel method based on the agglomerative technique to improve the community detection by finding the core node and the local community)
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摘要(中) 近年來,網路的快速發展與社群網路大量個崛起,造成現代人越來越依賴網路社群等相關軟體。過去在相關的社群探勘方面的研究,不外乎以K-means演算法的變形、凝聚法、圖形化的方式抑或是建立在Girvan-Newman所提出的演算法架構之下。其中凝聚法往往搭配著核心節點與區域社群的概念使用,其中的癥結點在於,利用核心節點與區域社群概念的凝聚法,往往會忽略掉在社群邊緣的節點,進而在做最後的分配時,未能將其作妥善的分群。因此,本研究基於現有的凝聚法相關研究,找出居中度及相關性作為新的凝聚依據,根據此二指標將社群作出妥善的分群,並與(Lim & Datta, 2013; Qiong & Ting-Ting, 2010; Tiantian & Bin, 2012)等學者我提出的方法作比較,進而證明其改善之效果。
摘要(英) Quick development of the Internet and huge explosion of the social network make people rely highly on the social network software in their daily life. Most researches on community detection in the past refer to K-means, agglomerative, graph or Girvan- Newman algorithm. The interest of this study has been directed to the algorithm of agglomerative. One possible deficiency of this method is that it always ignores the nodes which are on the edge of the community. Therefore, in the merging step, the nodes on the edge could be allocated to the wrong community. This study is aimed to improve the performance of the algorithm by finding the core node and the local community as new indexes for agglomerate. In the experiments, the results are compared with (Lim & Datta, 2013; Qiong & Ting-Ting, 2010; Tiantian & Bin, 2012) to show the effectiveness of the method developed in this study.
關鍵字(中) ★ 社群探勘
★ 核心節點
★ 區域社群
★ 凝聚法
★ 居中度
★ 相關性
關鍵字(英) ★ community detection
★ core node
★ local community
★ agglomerative
★ betweenness
★ relation
論文目次 論文摘要 i
Abstract ii
謝誌 iii
目錄 iv
圖目錄 vi
表目錄 viii
一、緒論 1
1-1 研究背景與動機 1
1-2研究目的 2
1-3 研究範圍與限制 2
1-4論文架構 3
二、相關研究 4
2-1 區域社群 4
2-2 節點之凝聚 6
2-3 圖形化分群 6
2-4 Girvan-Newman演算法 7
三、研究方法 12
3-1 系統架構與演算法 12
3-1-1核心節點的選取 14
3-1-2 關聯性 15
3-2 情境說明 18
四、實驗分析 22
4-1 實驗資料 22
4-2實驗評估指標 24
4-3 實驗結果 26
4-4 討論 34
五、結論 36
5-1結論與貢獻 36
5-2 未來研究方向 36
六、參考文獻 38
參考文獻

六、參考文獻
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指導教授 周世傑(Shih-chieh Chou) 審核日期 2014-7-11
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