博碩士論文 985202107 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:24 、訪客IP:3.17.156.114
姓名 宋泊儒(Bo-Ru Song)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 社群網路中多層次訊息傳播路徑探勘
(Mining Generalized Influential Propagation Paths from Social Network)
相關論文
★ 應用自組織映射圖網路及倒傳遞網路於探勘通信資料庫之潛在用戶★ 基於社群網路特徵之企業電子郵件分類
★ 行動網路用戶時序行為分析★ 社群網路中多階層影響力傳播探勘之研究
★ 以點對點技術為基礎之整合性資訊管理 及分析系統★ 在分散式雲端平台上對不同巨量天文應用之資料區域性適用策略研究
★ 應用資料倉儲技術探索點對點網路環境知識之研究★ 從交易資料庫中以自我推導方式探勘具有多層次FP-tree
★ 建構儲存體容量被動遷徙政策於生命週期管理系統之研究★ 應用服務探勘於發現複合服務之研究
★ 利用權重字尾樹中頻繁事件序改善入侵偵測系統★ 有效率的處理在資料倉儲上連續的聚合查詢
★ 入侵偵測系統:使用以函數為基礎的系統呼叫序列★ 有效率的在資料方體上進行多維度及多層次的關聯規則探勘
★ 在網路學習上的社群關聯及權重之課程建議★ 在社群網路服務中找出不活躍的使用者
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]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 近年來有大量的研究著重於利用使用者訊息在社群網路中傳播的行為,來評估社群之中個別使用者之影響力,以期利用來支援口碑行銷。然而在過往針對影響力評估之研究中,通常都只著重於使用者之間的傳播行為,並未考慮到使用者所屬社群之間的傳播行為。因此本研究為提供一個有系統性之訊息傳播規則評估方式,將依據如下三個模組來漸次的建構階層式訊息傳播路徑探勘系統,以期望從最細微之使用者訊息傳播規則到最巨觀之社群間傳播規則皆能發掘出來,以供企業做口碑行銷時使用。
本研究分為三個部分:第一部分將根據使用者過去所轉貼的文章,來定義使用者所屬的社群為何;第二部分利用序列探勘演算法找出社群網路中使用者之間訊息傳播之行為,並將使用者之間的傳播路徑以最合理之方式切割;第三部分設計一階層式字尾樹演算法,結合概念化階層探勘出不同細緻程度階層社群之間的訊息傳播規則。
最後,本研究將提出多種應用,可支援企業在網路行銷上的使用,能有效的滿足業者該如何以最小的預算,達到最大的行銷效益。
摘要(英) In order to evaluate the influence of single user in a society to support marketing, many researches focus on the behavior of propagating user messages in social network in recent years. However, most of the researches of influence evaluation in the past focused on the behavior of propagating between users, instead of considering the behavior of propagating between different social communities. Hence, this research provides a system to evaluate the pattern of propagating messages. We construct a hierarchical information propagating path mining system, expecting it could help discovering the pattern of information propagating paths from between users to between communities.
This research is composed of three parts: the first will define which social community the user belongs to by the articles he/she posted in the past; the second part we employ sequence mining algorithm to find the pattern of how users propagate information between each other, and cut the propagation path apart in a reasonable way; in the third part we design a Generalized Propagation Suffix Tree algorithm, combine it with concept hierarchy to discover the propagation paths of user or community in different granularity.
Finally, we propose some applications to support web marketing for enterprises to spent less cost to reach maximum benefits.
關鍵字(中) ★ 社群網路分析
★ 影響力傳播
★ 概念化階層
★ 字尾樹
關鍵字(英) ★ SNA
★ Suffix Tree
★ Concept Hierarchy
★ Information Propagation
★ Influence
論文目次 一、緒論
1-1 研究背景
1-1-1 社群網站
1-1-2 口碑行銷
1-2 研究動機與目的
1-3 論文架構
二、文獻探討
2-1 資料探勘
2-2 影響力與訊息傳播行為分析
2-3 權重式字尾樹
三、系統架構與研究對象
3-1 系統架構流程
3-2 研究對象─Digg網站
3-2-1 Digg 應用程式介面 (Digg API)
3-2-2 Web Crawler
四、問題定義
4-1 Digg社群網路
4-2 概念式階層
五、研究方法
5-1 使用者行為分析
5-2 探勘使用者訊息傳播路徑
5-2-1 訊息傳播路徑之定義
5-2-2 探勘步驟說明
5-2-3 傳播時間限制(Propagation path session)
5-3 廣義傳播字尾樹(Generalized Propagation Suffix Tree)
5-3-1 建構字尾樹
5-3-2 參數設定
5-3-3 廣義傳播字尾樹
六、應用
6-1 來源資料彙整
6-2 應用介紹
6-2-1 應用一
6-2-2 應用二
6-2-3 應用三
6-2-4 應用四
6-2-5 應用五
6-2-6 應用六
6-2-7 應用七
6-2-8 應用八
七、結論
參考文獻
參考文獻 〔1〕iBuzz research : http://i-buzzresearchcenter.blogspot.com .
〔2〕James Surowiecki, The Wisdom of Crowds, Doubleday, 2004.
〔3〕iBuzz research :超過八成的受訪者相信網路口碑,http://i-buzzresearchcenter.blogspot.com/2010/12/blog-post.html.
〔4〕Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, Second Edition, Elsevier Inc., San Francisco, 2006.
〔5〕Pedro Domingos, Matt Richardson, “Mining the network value of customers”, In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001.
〔6〕Matt Richardson, Pedro Domingos, “Mining Knowledge-Sharing Sites for Viral Marketing”, In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining.
〔7〕Jason Hartline, Vahab Mirrokni, Mukund Sundararajan, “Optimal marketing strategies over social networks”, In Proceeding of the 17th international conference on World Wide Web (WWW'08), 2008.
〔8〕Jure Leskovec, Lada A. Adamic, and Bernardo A. Huberman, “The Dynamics of Viral Marketing”, Transactions on the Web, 2007.
〔9〕Daniel Gruhl and R. Guha, “Information Diffusion Through Blogspace”, In Proceedings of the 13th International World Wide Web Conference, 2004.
〔10〕Meeyoung Cha, Alan Mislove, and Krishna P. Gummadi, “A Measurement-driven Analysis of Information Propagation in the Flickr Social Network”, In Proceedings of the 18th international conference on World Wide Web, 2009.
〔11〕Aris Anagnostopoulos, Ravi Kumar, and Mohammad Mahdian, “Influence and Correlation in Social Networks”, In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008.
〔12〕Parag Singla and Matthew Richardson, “Yes, There is a Correlation: From Social Networks to Personal Behavior on the Web”, In Proceeding of the 17th International Conference on World Wide Web, 2008.
〔13〕Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang, “Social Influence Analysis in Large-Scale Networks”, In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009.
〔14〕Gary William Flake, Steve Lawrence, and C. Lee Giles, “Efficient Identification of Web Communities”, In Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000.
〔15〕Jianshu Weng, Ee-Peng Lim, Jing Jiang, and Qi He, “TwitterRank: Finding Topic-Sensitive Influential Twitterers”, In Proceedings of the third ACM International Conference on Web Search and Data Mining, 2010.
〔16〕Amit Goyal, Francesco Bonchi, and Laks V.S. Lakshmanan, “Learning Influence Probabilities in Social Networks”, In Proceedings of the third ACM International Conference on Web Search and Data Mining, 2010.
〔17〕Masahiro Kimura, Kazumi Saito, Ryohei Nakano and Hiroshi Motoda, “Extracting Influential Nodes on a Social Network for Information Diffusion”, In Proceedings of Data Mining and Knowledge Discovery, 2010.
〔18〕Yu Wang, Gao Cong, Guojie Song and Kunqing Xie, “Community-Based Greedy Algorithm for Mining Top-K Influential Nodes in Mobile Social Networks”, In Proceedings of 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010.
〔19〕Tian Zhu, Bin Wu, and Bai Wang, “Social Influence and Role Analysis Based on Community Structure in Social Network”, In Proceedings of the fifth ADMA International Conference, 2009.
〔20〕吳彥慶,「利用權重字尾樹中頻繁事件序改善入侵偵測系統」,國立中央大學,碩士論文,民國96年。
〔21〕Digg, http://digg.com.
〔22〕Digg API, http://developers.digg.com/documentation.
〔23〕Crawljax, http://crawljax.com/.
〔24〕Wiki:http://zh.wikipedia.org/zh-hk/Wiki.
指導教授 蔡孟峰(Meng-Feng Tsai) 審核日期 2011-7-15
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