English  |  正體中文  |  简体中文  |  Items with full text/Total items : 69561/69561 (100%)
Visitors : 23616972      Online Users : 340
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65807


    Title: 對動態社群網路的階層式分群與視覺化設計;The Design of Hierarchical Clustering and Visualization Methodology for the Dynamic Social Network
    Authors: 吳帝華;Wu,Di-hua
    Contributors: 資訊工程學系
    Keywords: 社群網路分析;動態社群網路;遞增式分群;變化流模型;Social network analysis;Dynamic social network;Incremental clustering;Change stream model
    Date: 2014-08-26
    Issue Date: 2014-10-15 17:10:46 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 在社群網路分析的領域裡,將社群網路中個體的互動關係進行分群一直是個重要的議題。以往的研究中,大部分的分群方法都是在靜態或是涵蓋社群網路整體時間的概念下,而實際的社群網路是會經過時間而演化的,社群網路中個體的互動有可能在某段時間發生變化,進而影響社群網路中的社群結構,對於這樣演化的社群網路進行分群的話,效率便成為一個重要的問題。
    本研究共分兩個部分,第一部分為設計一個遞增分群(incremental clustering)方法,來解決分群效率的問題。將社群網路從傳統的快照圖模型(snapshot model)轉換成改變流模型(change stream model),並結合平衡式階層社群建立(balanced hierarchy construction)演算法,來提供一個遞增版本的平衡式階層社群建立演算法。在第二部分,對應於社群網路演化中分群所產生的結果,提供一個視覺化的設計,連結長條圖,這個視覺化方法是以社群為視點,來了解在暫時的時間下,不同階層時的社群關係。
    在實驗中,本研究透過分析Enron電子郵件,顯示出相對於原始的靜態分群方法,本研究提供的方法明顯的讓分群的效率有所改進。對於演化過程中暫時的社群結構,透過視覺化方法也可實際觀察出正確的分群結果,而利用比對於不同時間的結果,也能觀察出社群的變化。
    ;Detect the communities in social network by interactions of entities which is an important issue in social network analysis. Mostly, clustering algorithms are under static point or entire time of social networks concept. Real social networks usually evolve continuously with the passage of time. Interactions of entities might change at some point in time and make the community structures also change. Because of this situation, the problem of efficiently clustering appear.
    The research is composed of two parts: first of all, this paper present a design of incremental clustering to address the problem of efficiency. Transform the social network from traditional snapshot graph model to change stream model, and combine with balanced community hierarchy construction. Second, the design of visualization, connective bar chart, to satisfy the results created in dynamic social networks. The visualization is based on the view of communities and can understand the temporal view of the relationship among communities in different level.
    Experiment with the analysis of Enron e-mail, the results represent that our method can improve the efficiency apparently compare with the static method. The community structures also present a correct results and the difference between two periods by our design of visualization.
    Appears in Collections:[資訊工程研究所] 博碩士論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML442View/Open


    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明