博碩士論文 945402023 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:4 、訪客IP:34.204.191.0
姓名 王敏峰(Min-Feng Wang)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 社群網路中多階層影響力傳播探勘之研究
(Multi-Level Influence-Propagation Mining on 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
★ 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
★ 利用階層式權重字尾樹找出在天文觀測紀錄中變化相似的序列★ 漢字發音系統之音韻關聯規則探勘
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) 社群網路分析為一個能在不同情境下蒐集、分析及呈現社群間關係的方法。近年來由於社群網路服務的快速崛起及普及引領出新的網路消費族群,促使企業需要針對網際網路使用者,採用新的市場策略來發展及支援自身品牌、產品與服務在網路上的行銷。
此外,隨著社群網站和微部落格的興起,例如:Facebook、Digg及Twitter,大量使用者在這些網站上建立個人資料檔、分享生活經驗與個人興趣及建立主題社群,也造成人們的互動模式與消費行為改變,更使得許多計算機學家、行銷市場專家及社會科學學者對這些社群平台上的使用者訊息如何透過社群網路對其它用戶產生影響,及其資訊如何散播的現象產生極大的興趣。而其中又以發現與確認社群中每個使用者的影響力大小,以支援企業在市場行銷上的使用,來達到理想的行銷成果為最熱門之研究主題。
然而在過往研究中,大量的學者著重於利用統計及機率模組的方式來評估使用者訊息在社群網路中資訊散播的速度及涵蓋範圍,較缺乏從資料探勘的層面來探討使用者過往訊息的傳播行為;此外,亦未從較抽象化的思維來觀察使用者所屬社群之間的訊息傳播現象。因此本研究為提供一個有系統性之多階層訊息傳播規則評估方式,將依據如下步驟,漸次的探討社群網路中不同社群活動之訊息傳播行為。
第一、設計一個能夠支援時序性查詢及多階層探勘之演算法,以有效挖掘及查詢不同細緻程度之訊息傳播行為。
第二、針對社群網路中的多種不同主題性質之社群活動,設計一個分類方法來建構出該社群網路之概念化階層,以支援上述演算法來分析多階層社群活動。
本研究期望結合社群網路分析及資料探勘方法來建立具高彈性化之多階層社群網路分析方法,以供不同主題領域之專家能夠從最細微之使用者訊息傳播行為,到最巨觀之社群間傳播活動皆能有效觀察分析,以因應Web 2.0 興起後快速演變之網路使用者行為模式。
摘要(英) Social network analysis is a method for collecting, analyzing, and displaying community relationships under various scenarios. The growing pervasiveness of social network services has generated new customer groups enabling enterprises to adopt new marketing strategies for developing, marketing and supporting internet products and services. Well-known social networking websites such as Facebook, Twitter, and Digg allow users to construct personal profiles, share interesting information, and build relationships within a community. Interaction on social websites is rapidly affecting people’s social behaviors and habits. From our comprehensive survey of the literature on social networks showed that understanding the impact of communities in social networks requires complex computations. Furthermore, the few studies performed until now have focused on designing efficient and flexible platforms for mining the answers to questions in social networks to support social influence and relationship analysis, such as “What are the top-K direct influencing sources by a certain user or community?”, “Who is affected by a specific user or community at a given time?”, or even “What top-K users or communities generate the largest number of distinct paths during message propagation?”. This study therefore designed a flexible data mining algorithm for hierarchical analysis of the influence of social networks on emerging Web 2.0 technologies in order to analyze how this internet business model evolved.
The proposed method has two major components.
(1) A generalized suffix tree structure for analyzing information propagation patterns in communities with varying granularity.
(2) A classification module for classifying social activities based on user interests.
This work also designs a query interface for mining propagation paths and for discovering social network nodes that efficiently support enterprises in managing and analyzing social information. We look forward that the proposed mechanism has potential applications as a flexible social network platform for summarizing and analyzing network user behavior.
關鍵字(中) ★ 資料探勘
★ 影響力傳播
★ 社群網路分析
關鍵字(英) ★ Information diffusion
★ Data mining
★ Social network analysis
論文目次 Chapter 1. Introduction 1
1-1 Motivations 2
1-2 Contributions 4
1-3 Thesis Organization 4
Chapter 2. Background and Related Work 6
2-1 Social Network Analysis and Web 2.0 6
2-2 Influence Propagation and Information Dissemination 10
Chapter 3. System Framework 13
3-1 Social Activity Classification Model 14
3-2 Multi-Level Information Propagation Mining Model 16
3-3 Multi-Level Influence Dissemination Model 18
Chapter 4. Research Methods 19
4-1 Social Activity Classification 19
4-1.1 Fuzzy Association 20
4-1.2 Minimax-Similarity Algorithm 21
4-2 Multi-Level Information Propagation Mining 23
4-2.1 Problem Formulation 23
4-2.2 Propagation Path Discovery 25
4-2.3 Weighted Suffix Tree 29
4-2.4 Generalized Weighted Suffix Tree 32
Chapter 5. Experimental Results 40
5-1 Dataset Collection and Preprocessing 40
5-2 Experimental Results of Social Activity Model Classification Model 40
5-3 Experimental Results of Multi-Level Information-Propagation Mining 41
5-3.1 Dataset Collection and Preprocessing 41
5-3.2 Experimental Results of Multi-Level Information-Propagation Mining 42
Chapter 6. Applications 43
Chapter 7. Conclusions and Future Works 51
7-1 Conclusions 51
7-2 Future Works 52
Reference 54
參考文獻 [1] Anagnostopoulos, A., Kumar, R., and Mahdian, M., “Influence and correlation in social networks,” in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.7-15, 2008.
[2] Baeza-Yates, R. A. and Ribeiro-Neto, B, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA, 1999.
[3] Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D., ”Foundations of multidimensional network analysis,” in Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining, pp. 485-489, 2011.
[4] Buccafurri, F., Lax, G., “Improving similarity-based methods for information propagation on social networks,” Networked Digital Technologies Communications in Computer and Information Science, pp.391-401, 2010.
[5] Cai, D., Shao, Z., He, X.,Yan, X., and Han, J., “Mining hidden community in heterogeneous social networks,” in Proceedings of the 3rd International Workshop on Link Discovery, pp. 58 – 65, 2005.
[6] Cha, M., Mislove, A., and Gummadi, K.P., “A measurement-driven analysis of information propagation in the flickr social network,” in Proceedings of the 18th International Conference on World Wide Web, pp. 721-730, 2009.
[7] Chen, C., Yan, X., Zhu, F., Han, J., and Yu, P. S., “Graph OLAP: a multi-dimensional framework for graph data analysis,” Knowledge and Information Systems, vol. 21, pp. 41 – 63, 2009.
[8] Chen, V. H.-H. and Duh, H. B.-L, “Investigating user experience of online communities: The influence of community type,” in Proceedings of the IEEE International Conference on Social Computing, pp. 509-514, 2009.
[9] Chen, J., Zaiane, O.R., and Goebel, R., ”Detecting communities in large networks by iterative local expansion,” in Proceedings of the 2009 International Conference on Computational Aspects of Social Networks, pp.105-112, 2009
[10] Cheng, E., Grossman, J.W., Lipman, M.J., "Time-stamped graphs and their associated influence digraphs," Discrete Applied Mathematics, vol. 128, pp. 317-335, 2003.
[11] Christakis, N. A. and Fowler, J. H., "The spread of obesity in a large social network over 32 years," The New England Journal of Medicine, vol. 357, pp. 370-379, 2007.
[12] Corpet, F., “Multiple sequence alignment with hierarchical clustering," Nucleic Acids Research, pp. 10881-10890, 1988.
[13] Crandall, D. J., Cosley, D. , Huttenlocher D.P., Kleinberg, J. M. , and Suri, S., "Feedback effects between similarity and social influence in online communities," in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 160-168, 2008.
[14] Cuene, J., “Web 2.0: is it a whole new Internet,” Available at: http://cuene.typepad.com/MiMA.1.ppt, 2005.
[15] Domingos, P., and Richardson, M., ”Mining the network value of customers,” in Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining, pp.57–66, 2003.
[16] Dubois, M., Readings in Fuzzy Sets for Intelligent Systems. Morgan Kaufmann Publishers Inc. 1993.
[17] eMarketer Digital Intelligence, Facebook Drives US Social Network Ad Spending Past $3 Billion in 2011, Available at: http://www.emarketer.com/Article.aspx?R=1008180.
[18] Goyal, A., Bonchi, F., and Lakshmanan, L.V. S., “Learning influence probabilities in social networks,” in Proceedings of the 3th ACM International Conference on Web Search and Data Mining, pp. 241-250, 2010.
[19] Ghosh, R., and Lerman, K., “Predicting influential users in online social networks,” in Proceedings of Knowledge Discovery and Data Mining Workshop on Social Network Analysis, 2010.
[20] Hartigan, J. A. and Wong, M. A., “Algorithm AS 136: A k-means clustering algorithm,” Journal of the Royal Statistical Society, pp. 100-108, 1979.
[21] Hildrum, K. and Yu, P. S., “Focused community discovery,” in Proceedings of the Fifth IEEE International Conference on Data Mining, pp. 641-644, 2005.
[22] Kimura, M., Saito, K., Nakano, R., and Motoda, H., “Extracting influential nodes for information diffusion on a social network,” in Proceedings of the 22th AAAI Conference on Artificial Intelligence, pp. 1371-1376, 2007.
[23] Kimura, M., Yamakawa, K., Saito, K., and Motoda, H., “Community analysis of influential nodes for information diffusion on a social network,” in Proceedings of the International Joint Conference on Neural Networks, pp.1358-1363, 2008.
[24] Kwak, H., Lee, C., Park, H., and Moon, S., "What is Twitter, a social network or a news media?," in Proceedings of the 19th International Conference on World Wide Web, pp. 591-600, 2010.
[25] Lerman, K., and Ghosh, R., “Information contagion: An empirical study of the spread of news on Digg and Twitter social networks,” in Proceedings of the Forth International Conference on Weblogs and Social Media, 2010.
[26] Leskovec, J., Adamic, L. A., Huberman, B. A., "The dynamics of viral marketing," ACM Transactions on the Web, vol. 1, 2007.
[27] Magnani, M., Montesi, D., and Rossi, L., “Information propagation analysis in a social network site,” in Proceedings of the International conference on Advances in Social Network Analysis and Mining, pp. 296-300, 2010.
[28] Newman, M. E. J., “Finding community structure in networks using the eigenvectors of matrices,” Physical Review E, 2006.
[29] O’Reilly, T., “What is Web 2.0 design patterns and business models for next generation of software,” Available at: http://oreilly.com/web2/archive/what-is-web-20.html, 2005
[30] Richardson, M., Domingos, P., “Mining knowledge-sharing sites for viral marketing,” in Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61-70, 2002.
[31] Takeuchi, S., Kondoh, T., Akiyoshi, M., and Komoda, N., “Evaluation of similarities of propagation forms on social network for extracting relationships of information,” in Proceedings of the International Symposium on Applications and the Internet, pp. 341-344, 2008.
[32] Tang, J., Sun, J., Wang, C., and Yang, Z., “Social influence analysis in large-scale networks,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 807-816, 2009.
[33] Tantipathananandh, C, Berger-Wolf, T, and Kempe, D., “A framework for community identification in dynamic social networks,” in Proceedings of the 13th ACM SIGKDD International conference on Knowledge Discovery and Data Mining, pp. 717-726, 2007.
[34] Wang, Y., Cong, G., Song, G., Xie, K., “Community-based greedy algorithm for mining top-k influential nodes in mobile social networks,” in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1039-1048, 2010.
[35] Wang, M.-F., Kuo, Y.-L., Tsai, M.-F., Tang, C.-H., Huang, K., “Hierarchical role classification based on social behavior analysis,” in Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia, pp.426-429, 2010.
[36] Wang, M.-F., Wu, Y.-C., and Tsai, M.-F., “Exploiting frequent episodes in weighted suffix tree to improve intrusion detection system,” in Proceedings of the 22th International Conference on Advanced Information Networking and Applications - Workshops, pp. 1246-1252, 2008.
[37] Weiner, P., "Linear pattern matching algorithm," in Proceedings of the 14th IEEE Symposium on Switching and Automata Theory, pp. 1–11, 1973.
[38] Weng, J., Lim, E.-P., Jiang, J., and He. Q., “TwitterRank: finding topic-sensitive influential twitterers,” in Proceedings of the 3th ACM International Conference on Web Search and Data Mining, pp. 261-270, 2010.
[39] Yang, W-S and Dia, J-B., “Discovering cohesive subgroups from social networks for targeted advertising,” Expert Systems with Applications, vol. 34, pp. 2029 – 2038, 2008
[40] Zadeh, L. A., "Fuzzy sets," Information and Control, vol. 8, pp. 338–353, 1965.
[41] Zaiane, O.R., Chen, J., and Goebel, R., “DBconnect: mining research community on DBLP data,” in Proceedings of the 9th WebKDD and 1st SNA-KDD Workshop on Web mining and Social Network Analysis, pp. 74 – 81, 2007.
[42] Zhang, J., Tang, J., Liang, B., Yang, Z., Wang, S, Zuo, J., and Li, J., “Recommendation over a heterogeneous social network,” in Proceedings of the 2008 International Conference on Web-Age Information Management, pp. 309 – 316, 2008.
指導教授 蔡孟峰(meng-feng tsai) 審核日期 2012-8-19
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