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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 莊裕澤(Yuh-Jzer Joung) | |
dc.contributor.author | Shih-Wei Lo | en |
dc.contributor.author | 羅仕煒 | zh_TW |
dc.date.accessioned | 2021-06-13T00:22:59Z | - |
dc.date.available | 2007-07-31 | |
dc.date.copyright | 2007-07-31 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-07-27 | |
dc.identifier.citation | [1] Friendster. http://www.friendster.com/.
[2] iknow. http://www.spcomm.uiuc.edu/teclab/iknow/teclab.html/. [3] Yam. http://blog.yam.com/. [4] Eytan Adar and Lada A. Adamic. Tracking information epidemics in blogspace. In Proceedings of the The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2005), pages 207–214,Washington, DC, United States, 2005. IEEE Computer Society. [5] Eytan Adar, Li Zhang, Lada A. Adamic, and Rajan M. Lukose. Implicit structure and the dynamic of blogspace. In Proceedings of the first Annual Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics (WWE 2004), 2004. [6] Anjo Anjewierden, Robert de Hoog, Rogier Brussee, and Lilia Efimova. Detecting knowledge flows in weblogs. In Proceedings of the thirteenth International Conference on Conceptual Structures (ICCS 2005). Kassel university press, 2005. [7] Stephen P. Borgatti and Rob Cross. A relational view of information seeking and learning in social networks. Management SCI, 49(4):432–445, 2003. [8] Jacqueline Johnson Brown and Peter H. Reingen. Social ties and word-of-mouth referral behavior. Journal of Consumer Research: An Interdisciplinary Quarterly, 14(3):350–362, December 1987. [9] Tanzeem Roy Choudhury and Alex Pentland. Characterizing social networks using the sociometer. In Proceedings of the North American Association for Computational Social and Organizational Science (NAACSOS 2004), Pittsburgh, Pennsylvania, United States, June 2004. [10] Robin Cowan and Nicolas Jonard. Network structure and the diffusion of knowledge. Journal of Economic Dynamics and Control, 28(8):1557–1575, June 2004. [11] Lilia Efimova and Stephanie Hendrick. In search for a virtual settlement: An exploration of weblog community boundaries. none, 2005. [12] L.C. Freeman. A set of measures of centrality based upon betweenness. Sociometry, pages 35–41, 1977. [13] Linton C. Freeman. Centrality in social networks conceptual clarification. Social Networks, 1(3):215–239, 1979. [14] Mary K. Fuller and E. Burton Swanson. The diffusion of information centers: patterns of innovation adoption by professional subunits. In SIGCPR ’92: Proceedings of the 1992 ACM SIGCPR conference on Computer personnel research, pages 370–387, New York, NY, USA, 1992. ACM Press. [15] Michelle Girvan, Duncan S. Callaway, M. E. J. Newman, and Steven H. Strogatz. Simple model of epidemics with pathogen mutation. Phys. Rev. E, 65(3):031915, Mar 2002. [16] Jacob Goldenberg, Barak Libai, and Eitan Muller. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12(3):211–223, 2001. [17] Mark S. Granovetter. The strength of weak ties. The American Journal of Sociology, 78(6):1360–1380, 1973. [18] Daniel Gruhl, R. Guha, David Liben-Nowell, and Andrew Tomkins. Information diffusion through blogspace. In Proceedings of the thirteenth international conference on World Wide Web (WWW 2004), pages 491–501, New York, NY, United States, 2004. ACM Press. [19] Jack Hebert. Predicting information flow through blogspace. Retrieved December 18, 2006 from http://www.cs.washington.edu/homes/jhebert/pageBlog/blogspace.html. [20] David Kempe, Jon Kleinberg, and Eva Tardos. Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2003), pages 137–146, New York, NY, United States, 2003. ACM Press. [21] Abdelmajid Khelil, Christian Becker, Jing Tian, and Kurt Rothermel. An epidemic model for information diffusion in manets. In MSWiM ’02: Proceedings of the 5th ACM international workshop on Modeling analysis and simulation of wireless and mobile systems, pages 54–60, New York, NY, USA, 2002. ACM Press. [22] Sergio Marti, Prasanna Ganesan, and Hector Garcia-Molina. DHT routing using social links. In Proceedings of the third International Workshop on Peer-to-Peer Systems (IPTPS 2004), Lecture Notes in Computer Science, pages 100–111. Springer-Verlag, 2004. [23] Sergei Maslov, Kim Sneppen, and Alexei Zaliznyak. Pattern detection in complex networks: Correlation profile of the internet. Arxiv preprint cond-mat/0205379, 2002. [24] Mark E J Newman and Jeffrey Park. Why social networks are different from other types of networks. American Physical Society Physical Review E, 68(3):036122, 2003. [25] Everett M. Rogers. Diffusion of Innovations. Free Press, fourth edition, 1995. [26] Thomas W. Valente. Social network thresholds in the diffusion of innovations. Social Networks, 18(1):69–89, 1996. [27] Stanley Wasserman, Katherine Faust, and Dawn Iacobucci. Social Network Analysis: Methods and Applications. Cambridge University Press, third edition, 1994. [28] DJ Watts and SH Strogatz. Collective dynamics of’small-world’networks. Nature, 393(6684):409–10, 1998. [29] Duncan J. Watts, Peter Sheridan Dodds, and Mark E J Newman. Identity and search in social networks. Science, 296:1302, 2002. [30] Barry Wellman. For a social network analysis of computer networks: a sociological perspective on collaborative work and virtual community. In Proceedings of the ACM SIGCPR/SIGMIS conference on Computer personnel research (SIGCPR 1996), pages 1–11, New York, NY, USA, 1996. ACM Press. [31] Barry Wellman. Computer networks as social networks. Science, 293(5537):2031–2034, 2001. [32] Bin Yu, Mahadevan Venkatraman, and Munindar P. Singh. An adaptive social network for information access: Theoretical and experimental results. Applied Artificial Intelligence, 17(1):21–38, 2003. [33] Jun Zhang and Mark S. Ackerman. Searching for expertise in social networks: a simulation of potential strategies. In Proceedings of the international ACM SIGGROUP conference on Supporting group work (SIGGROUP 2005), pages 71–80, New York, NY, USA, 2005. ACM Press. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28792 | - |
dc.description.abstract | 部落格是近年來在網際網路上發展的網路服務之一。使用者可以透過簡單的操作方式發佈各種類型的資訊,將資訊擴散到不同的部落格內。透過清楚的時間記錄,資訊擴散的方向與先後順序可以被清楚的定義出來,透過部落格上的文章內容及其回應與引述功能,也能了解不同部落格之間的關係。在部落空間內,資訊擴散可以是被動或主動地完成,在此,我們提供一可主動資訊擴散的模型,藉以改善部落空間內整體的資訊擴散效能。
透過實際資料以及實驗模擬,我們比較各種不同的主動擴散策略之成效;使用者透過不同的資訊擴散策略可達成其個別目標。分析單一主動擴散之效能亦可清楚了解個別主動擴散對於整體擴散速率的影響。本實驗並嘗試提供分析主動資訊擴散成本的方法,以便更為客觀地分析節點在主動資訊擴散上的效益。 | zh_TW |
dc.description.abstract | Weblog is a journal that is frequently updated and normally reflecting the views of the blog’s creator. Users can easily create content in any type of information. Blog users create content and deliver their thought in their blogs. Information diffusion in blospace is traceable because of time records. we can also realize the relationship between blogs through the links and
articles in the blogspace. Diffusion of information can be both active and passive diffusion in blogspace. Information flows among blogs and the diffusion is obvious. We provide a new model for information diffusion in blogspace. Efficiency of diffusion rate raises up under active information diffusion. We simulate the diffusion process in blogspace and compare the efficiency of active information diffusion under different strategies. Different strategy leads to different outcome; diffusers can select a strategy to reach their own goals. We also analyze the efficiency of active diffusion under different strategies. The efficiency of active diffusion greatly affects the speed of information diffusion. Finally, we provide a method to evaluate if the active diffusion is worthy to each node. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T00:22:59Z (GMT). No. of bitstreams: 1 ntu-96-R94725034-1.pdf: 1178683 bytes, checksum: 6db5bdc6495e49cbcaf931610dfb16ec (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | 1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Related Work 8 2.1 Introduction of Social Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.1 Property of Social Network . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.2 Social Network Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Computer Network and Social Network . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Searching in Social Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4 Research about information diffusion . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4.1 The theory of information diffusion . . . . . . . . . . . . . . . . . . . . 11 2.4.2 Two Information DiffusionModel . . . . . . . . . . . . . . . . . . . . . . 12 2.4.3 Relationship between social ties and information diffusion . . . . . . . . . 12 2.5 information diffusion in Blogspace . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5.1 Information Diffusion In Blogspace . . . . . . . . . . . . . . . . . . . . . 13 2.5.2 Implicit Structure in blogspace . . . . . . . . . . . . . . . . . . . . . . . 13 2.5.3 KnowledgeManagement Through Blogspace . . . . . . . . . . . . . . . . 14 2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3 System Definition 16 3.1 SimulationModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.1.1 Linear ThresholdModel . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.1.2 Independent CascadeModel . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2 Node Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2.1 Nodes in blogspace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2.2 Behavior of nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2.3 Status of nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3 Tie Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.1 Definition of Tie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.2 Parameters of Tie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.4 Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.4.1 Definition of Information . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.4.2 Information in blogspace . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.5 Active Information Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.5.1 Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.5.2 Waste of Active Information Diffusion . . . . . . . . . . . . . . . . . . . 28 3.5.3 Cost of Active Information Diffusion . . . . . . . . . . . . . . . . . . . . 29 3.6 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.6.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.6.2 Collection of Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.6.3 Collection of Ties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4 Experiment Results 35 4.1 Verify themodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.1.1 The Real Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.1.2 Verify the Experiment Model . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Comparison of Diffusion Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.2.1 Diffusion Rate Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.2.2 The Tipping Point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.2.3 Brief Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3 Utility of Active Information Diffusion . . . . . . . . . . . . . . . . . . . . . . . 42 4.3.1 Cumulative Frequency of Active Diffusion and Waste of Active Diffusion 42 4.3.2 The Utility of Active Information Diffusion . . . . . . . . . . . . . . . . . 43 4.4 Analysis of Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.4.1 Analysis of Identical Information . . . . . . . . . . . . . . . . . . . . . . 45 4.5 Analysis of Diffusion Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.5.1 Cost Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5 Conclusion and Future Work 49 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.2 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Bibliography 51 | |
dc.language.iso | en | |
dc.title | 部落格內之主動資訊擴散模型 | zh_TW |
dc.title | An Active Information Diffusion Model In Blogspace | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李瑞庭(Jui-Tine Lee),蔡益坤(Yih-Kuen Tsay),陳炳宇(Bing-Yu Chen) | |
dc.subject.keyword | 社會網路,資訊擴散,部落,格,策略,主動擴散, | zh_TW |
dc.subject.keyword | Social Network,Information Diffusion,Blog,Strategy, | en |
dc.relation.page | 53 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2007-07-27 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
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