博碩士論文 101423050 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:5 、訪客IP:3.138.199.50
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)
相關論文
★ 信用卡盜刷防治簡訊規則製作之決策支援系統★ 不同檢索策略之效果比較
★ 知識分享過程之影響因子探討★ 兼具分享功能之檢索代理人系統建構與評估
★ 犯罪青少年電腦態度與學習自我效能之研究★ 使用AHP分析法在軟體度量議題之研究
★ 優化入侵規則庫★ 商務資訊擷取效率與品質促進之研究
★ 以分析層級程序法衡量銀行業導入企業應用整合系統(EAI)之關鍵因素★ 應用基因演算法於叢集電腦機房強迫對流裝置佈局最佳近似解之研究
★ The Development of a CASE Tool with Knowledge Management Functions★ 以PAT tree 為基礎發展之快速搜尋索引樹
★ 以複合名詞為基礎之文件概念建立方式★ 利用使用者興趣檔探討形容詞所處位置對評論分類的重要性
★ 透過半結構資訊及使用者回饋資訊以協助使用者過濾網頁文件搜尋結果★ 利用feature-opinion pair建立向量空間模型以進行使用者評論分類之研究
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
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
Albert, R., Jeong, H.& Barabási, A.-L. (1999). Internet: Diameter of the world-wide web. Nature, 401(6749), 130-131.
Amaral, L. A. N., Scala, A., Barthelemy, M.& Stanley, H. E. (2000). Classes of small-world networks. Proceedings of the National Academy of Sciences, 97(21), 11149-11152.
Bagrow, J. P. (2008). Evaluating local community methods in networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(05).
Bagrow, J. P.& Bollt, E. M. (2005). Local method for detecting communities. Physical Review E, 72(4).
Breiger, R. L., Boorman, S. A.& Arabie, P. (1975). An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. Journal of mathematical psychology, 12(3), 328-383.
Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., . . . Wiener, J. (2000). Graph structure in the web. Computer networks, 33(1), 309-320.
Chira, C.& Gog, A. (2011). Collaborative Community Detection in Complex Networks. In E. Corchado, M. Kurzyński & M. Woźniak (Eds.), Hybrid Artificial Intelligent Systems (Vol. 6678, pp. 380-387): Springer Berlin Heidelberg.
Clauset, A. (2005). Finding local community structure in networks. Physical Review E, 72(2).
Faloutsos, M., Faloutsos, P.& Faloutsos, C. (1999). On power-law relationships of the Internet topology. SIGCOMM Comput. Commun. Rev., 29(4), 251-262.
Girvan, M.& Newman, M. E. (2002). Community structure in social and biological networks. In Proceedings of the National Academy of Sciences, 99(12), 7821-7826.
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N.& Barabási, A.-L. (2000). The large-scale organization of metabolic networks. Nature, 407(6804), 651-654.
Jiyang, C., Zaiane, O.& Goebel, R. (2009). Local Community Identification in Social Networks. Paper presented at the Social Network Analysis and Mining,. ASONAM ′09. International Conference on Advances in Athens Greece.
Kernighan, B. W.& Lin, S. (1970). An efficient heuristic procedure for partitioning graphs. Bell system technical journal, 49(2), 291-307.
Kleczkowski, A.& Grenfell, B. T. (1999). Mean-field-type equations for spread of epidemics: The ‘small world’model. Physica A: Statistical Mechanics and its Applications, 274(1), 355-360.
Li, W., Yang, J.-Y.& Hadden, W.-C. (2009). Analyzing complex networks from a data analysis viewpoint. Europhysics Letters, 88(6).
LI, W., YANG, X., ZHANG, C., TANG, K.& YANG, J. (2011). A clustering method for community detection on complex networks. CAAI Transactions on Intelligent Systems, Harbin, China.
Lim, K. H.& Datta, A. (2013). A seed-centric community detection algorithm based on an expanding ring search. Paper presented at the Proceedings of the First Australasian Web Conference-Volume 144.
Luo, F., Wang, J. Z.& Promislow, E. (2008). Exploring local community structures in large networks. Web Intelligence and Agent Systems, 6(4), 387-400.
Lusseau, D. (2003). The emergent properties of a dolphin social network. Proceedings of the Royal Society of London. Series B: Biological Sciences, 270(Suppl 2), 186-188.
Marchiori, M.& Latora, V. (2000). Harmony in the small-world. Physica A: Statistical Mechanics and its Applications, 285(3), 539-546.
Moore, C.& Newman, M. E. (2000). Epidemics and percolation in small-world networks. Physical Review E, 61(5).
Newman, M. E. (2001). The structure of scientific collaboration networks. In Proceedings of the National Academy of Sciences, 98(2), 404-409.
Newman, M. E.& Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2).
Pastor-Satorras, R.& Vespignani, A. (2001). Epidemic spreading in scale-free networks. Physical review letters, 86(14).
Pizzuti, C. (2008). GA-Net: A genetic algorithm for community detection in social networks, Parallel Problem Solving from Nature–PPSN X (edition 10th, pp. 1081-1090): Springer.
Qiong, C.& Ting-Ting, W. (2010). A method for local community detection by finding maximal-degree nodes. Paper presented at the Machine Learning and Cybernetics (ICMLC), International Conference in Qingdao China.
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V.& Parisi, D. (2004). Defining and identifying communities in networks. In Proceedings of the National Academy of Sciences of the United States of America, 101(9), 2658-2663.
Redner, S. (1998). How popular is your paper? An empirical study of the citation distribution. The European Physical Journal B-Condensed Matter and Complex Systems, 4(2), 131-134.
Scott, J. (2000). Social Network Analysis, A Handbook, Sage Publication.
Tiantian, Z.& Bin, W. (2012). A Method for Local Community Detection by Finding Core Nodes. Publish on Advance in Social Networks Analysis and mining(ASONAM), 1171-1176.
Wagner, A.& Fell, D. A. (2001). The small world inside large metabolic networks. Proceedings of the Royal Society of London. Series B: Biological Sciences, 268(1478), 1803-1810.
Watts, D. J.& Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. Nature, 393(6684), 440-442.
Weinstein, C., Campbell, W., Delaney, B.& O′Leary, G. (2009). Modeling and detection techniques for Counter-Terror Social Network Analysis and Intent Recognition. Paper presented at the Aerospace conference IEEE.
Zachary, W. (1977). An Information Flow Modelfor Conflict and Fission in Small Groups1. Journal of anthropological research, 33(4), 452-473.
Zhang, S., Wang, R.-S.& Zhang, X.-S. (2007). Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A: Statistical Mechanics and its Applications, 374(1), 483-490.
指導教授 周世傑(Shih-chieh Chou) 審核日期 2014-7-11
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明