社群網路服務（SNS）已經成為人們相互溝通的一個主要的連網服務。這是描述人們在現實與虛擬世界之間的複雜關係。不同的社群網路有不同的特點和不同的影響程度。為了理解訊息傳播的過程中，它的社會影響後面的驅動的力量，本文通過分析社交網路節點之間所呈現的訊息傳播模型上的狀態來做詳細的分析。本論文提出4種的訊息傳播模型，其目的是來描述社群網路服務中訊息傳播的特性。我們分析了訊息的傳播模型和進而了解訊息如何在社群網路中快速的傳播。我們也分析社群群聚模式的意義和社群叢集參數的影響。此外，也建置了Hadoop平台來作社群網路的大資料分析，這平台是用普通電腦硬體所建立的大型運算集群上以並行處理的方式來分析從Facebook收集來的海量用戶數據。進而驗證我們提出的模型和做一些訊息傳播模型的修訂，最後提出一個修訂的訊息傳播模型來更準確的驗證社群網路的一些隱含特性。我們相信，我們的研究可在未來的社群網路服務的研究中提出有價值的見解。

Social network services (SNS) have become a major internet service for people to communicate with each other. It is full of complex relationships among people in the real-life and virtual world. Different social networks have different characteristics and varying levels of influence. To understand the message propagation process, the driving power behind it and its social influence, this paper presents a detailed analysis of message propagation models over the social networks by analyzing the relationships among nodes. This paper presents four proposed models which aim to analyze message propagation on social networks. We analyze the message propagation models and show how messages spread through the social networks. We also analyze and describe the significance and influence of clustering parameters in the model on the social clusters. Furthermore, we propose an social network analysis on Hadoop platform and implement a prototype to verify the social network characteristics. Hadoop platform can process massive data in a parallel manner on a large cluster built by commodity hardware. We also present a measurement study of messages collected from 900K users on Facebook, to verify our proposed models by means of big-data Hadoop platform. We believe that our research provides valuable insights for future social network service research.

Table of Contents
論文審定書 i
誌謝 iii
摘要 iv
Table of Contents vi
List of Figures vii
List of Tables x
List of Symbols xi
Abbreviations xii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Research Objective 2
1.3 Contribution and Organization 2
Chapter 2 Related Works 5
2.1 Social Network Services 5
2.2 Structures of Social Networks 9
2.3 The Social Influence-Related Factors 12
2.4 The Measuring Method of Social Network Influence 16
2.5 The Propagation of Influence on Social Networks 37
2.6 Cloud Computing 45
Chapter 3 Message Propagation Model (MPM) for Social Networks 50
3.1 RM Model 53
3.2 RMR Model 58
3.3 RMO Model 63
3.4 RMI Model 72
3.5 The Revised MPM Model(RLHmI model) 74
3.6 The Analysis of Social Network Clustering 78
Chapter 4 Experiments and Simulation Results 91
4.1 Social Clustering Experiments 91
4.2 MPM Model Experiments and Simulations 97
4.3 Experiments of Social Network Analyses with Hadoop Platform 112
4.4 Observations and Discussions 124
Chapter 5 Conclusions 131
References 135

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