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研究生:楊士慧
研究生(外文):Shih-Hui Yang
論文名稱:社群網路對數成長模型
論文名稱(外文):A Logistic Growth Model for Social Networks
指導教授:李瑞庭李瑞庭引用關係
口試委員:陳正綱林妙聰
口試日期:2011-06-10
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊管理學研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:35
中文關鍵詞:社群網路成長模型無尺度網路對數成長模型
外文關鍵詞:Social networkgrowth modelscale-free networklogistic growth model
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隨著Web 2.0技術的發展,許多社群網路(如: Facebook, Twitter, 與Digg等)蓬勃發展,了解這些社群網站的成長模型與結構特徵,不但有助於提升經營網站的技術,亦可增進網站的價值,以及更有效地計劃與執行管理策略。因此,在本篇論文中,我們利用網站外部與內部吸引力,提出了社群網站的對數成長模型,來描述社群網站的成長模式與分析其網路特徵。本篇論文可分為三個部份。首先,我們利用網站外部與內部吸引力,提出一個社群網站成長模型;接著,分析模型的特性,並証明所提出的模型具無尺度網路的特徵;最後,我們利用真實社群網站的資料評估所提出的模型,結果顯示,我們所提出的模型可解釋真實世界的社群網站的成長模式及結構特徵,並藉由此模型提出管理上的應用與策略,以提升社群網站的價值。

With advance of Web 2.0 technology, many social networks such as Facebook, Twitter, and Digg, have been highly developed in recent years. Understanding the growth patterns and the characteristics of social networks helps us to promote the technology of running social networks, increase the networks’ value, and formulate marketing and pricing strategies. Therefore, in this thesis, we first utilize the concept of internal and external attractions to propose a population growth model. Next, we analyze the properties of the proposed model and show that the model has the characteristics of sale-free networks. Finally, we collect the data from two real world social networks to evaluate the proposed model. The experimental results show that these two social networks can be well fitted by the proposed model. Furthermore, we address the management implications of the proposed model and discuss how to promote the value of social networks.

Table of Contents
Table of Contents i
List of Figures ii
Chapter 1 Introduction 1
Chapter 2 Preliminaries and Problem Definitions 4
Chapter 3 The Proposed Model 5
3.1 Degree distribution 8
3.2 Degree correlation 9
3.3 Clustering coefficient 10
3.4 Diameter 11
Chapter 4 Numerical Study 13
Chapter 5 Conclusions and Future Work 23
References 26
Appendix A 29
Appendix B 32

List of Figures
Figure 1. The number of nodes in a social network. 6
Figure 2. The number of users in Facebook. 14
Figure 3. The value of αt for Facebook. 15
Figure 4. The number of users in Digg. 15
Figure 5. The value of αt for Digg. 16
Figure 6. The value of βt for Facebook. 17
Figure 7. The value of βt for Digg. 18
Figure 8. Diameter at each step for Facebook. 19
Figure 9. Diameter at each step for Digg. 19
Figure 10. Average friends by join time for Facebook. 20
Figure 11. Average friends by join time for Digg. 21













List of Tables
Table 1. Notations of the proposed model. 5


References

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