“在當今世界，社會結構就是一切，這（臉譜）是的東西。 ”
 〜肖恩·帕克，由安德魯·加菲爾德在社交網絡發揮
我們生活在一個社會的世界。文化，社會對組織影響力，它的社交網絡，無論是內部和外部。我們知道，社會結構多麼強大：從影響的思想，文化和社會規範，以種族和階層的人的刻板印象。我們也每天都看到社交網絡的影響力，從互聯網到我們的日常生活。
早前，我們做了一個有趣的觀察基於啟發式：為什麼人們誰是當前學生或校友的長青藤聯盟或其他受歡迎的學校往往在生活中做的很好？無論是在商業，政治等。我們進一步思考這個問題，並注意到一個有趣的趨勢：具體的學校歷屆畢業生必須投資於初創他們的後輩開始的強烈傾向。
例子是豐富，尤其是在常青藤大學的地位的情況下：谷歌收到了他們的天使資金來自安迪Bechtosheim ， Sun微系統的聯合創始人和博士在斯坦福大學的電子工程;雅虎的早期幾輪融資分別由邁克爾·莫里茨紅杉資本，賓夕法尼亞大學的校友。 Facebook的天使投資是由彼得·泰爾，是斯坦福大學的校友。
這是巧合？還是有其他的力量在起作用？如一個老同學，同學關係網？在本研究中，我們試圖理解這些趨勢，並建立預測模型。

“In a world where social structure is everything, this (Facebook) was the thing.”
~ Sean Parker , played by Andrew Garfield in The Social Network
We live in a social world. Cultures, societies to organizations are influenced by it’s social network, be it internally and externally. We know how powerful social structures are: from influencing thoughts, cultural and social norms to stereotyping of races and class of people. We also see the influence of social networks everyday, from the Internet to our daily life.
Sometime ago, we made an interesting observation based on heuristics: why do people who were current students or alumni’s of Ivy Leagues or other popular schools tend to do well in life? Be it in businesses, politics and so on. We further think through this issue and noticed an interesting trend: alumnis of specific schools have a strong tendency to invest in startups started by their juniors.
Examples are aplenty, especially in the case of universities of Ivy League status: Google received their angel funding from Andy Bechtosheim, co-founder of Sun Microsystems and PhD in Stanford’s electrical engineering; Yahoo!’s early financing rounds was led by Michael Moritz of Sequoia Capital, alumni of University of Pennsylvania. Facebook’s angel investment was made by Peter Thiel, a Stanford alumni.
Are these coincidences? Or are there other forces at work? Such as an old school-boy network? In this research, we sought to understand these trends and build a predictive model.

Table of Contents
CHAPTER 1: INTRODUCTION 1
1.1 Introduction 1
1.2 Contribution to Literature 2
1.2.1 Modeling prediction of investment behavior as a link prediction problem 2
1.2.2 Combining multiple link prediction techniques to gain greater insight of social networks 2
1.2.3 Providing general rules of thumb for companies seeking investment 3
1.3 Research Structure 3
CHAPTER 2: RELATED WORK 7
2.1 Survey of Related Work 7
2.2 Related Research on Investment Behaviors 7
2.3 Related Research on Social Network Analysis 8
2.4 Other Related Research 9
2.5 Link Prediction as a Model to Predict Investor Behavior 9
CHAPTER 3: PROSPERITY TAIWAN 11
3.1 Prosperity Taiwan Project Backgroud 11
3.2 Taiwan’s Economic Strengths and Current Economic Landscape 11
3.3 The “Prosperity” in Prosperity Taiwan 12
3.4 Vision of Prosperity Taiwan 12
3.5 Culture, Arts and Creativity as an Example 12
3.5 Intelligent Service Machines to aid Economic Transformation 13
3.5.1 The V+ Platform 14
CHAPTER 4: METHOLOGY 17
4.1 Methodology 17
4.2 Dataset 20
4.2.1 CrunchBase Dataset 20
4.2.2 Data Selection 22
4.3 Concepts, Definitions and Examples 23
4.3.1 People 23
4.3.2 Companies 24
4.3.3 Financial Organization 24
4.3.4 Investors 24
4.3.5 Social Graph 24
4.3.5 Investment Graph 25
4.3.6 More definitions 26
4.4 Social Behavior of Investors in Facebook’s Small World 28
4.4.1 Understand Social Behavior using Descriptive Mining 29
4.4.2 Shortest Path 30
4.4.3 Adamic/Adar 33
4.4.3 Jaccard Coefficient 35
4.4.4 Common Neighbors 38
4.4.5 Preferential Attachment 39
4.4.6 Number of Shortest Paths between Investor and Company 40
4.4.7 Where’s the Money? Guidelines for Seeking Investments. 41
4.4.8 Summary of Intuition 41
4.5 Investors Are Social Animals: Modeling Investment Behavior as a Link Prediction Problem 41
4.5.1 Modeling Social Relationship 43
4.5.2 Learning Algorithms 43
4.5.3 Significance of Methodology 44
4.6 Experiment Setup 45
4.6.1 Evaluation Metrics 45
4.6.2 Evaluation 45
4.6.3 Cross Comparison of Performance Across Different Learning Algorithms 46
4.6.4 Ground Truth Labels 46
4.6.5 Data Split for Training and Testing 46
4.6.6 Experiment Runs 46
CHAPTER 5: EXPERIMENTS 48
5.1 Experiment Result 48
5.1.1 Aggregate Performance 48
5.1.2 Industry Performance 50
5.1.3 Summary of Performance Categorical Performance 53
5.2 General Performance 53
5.2.1 Visualizing the Decision Process 54
CHAPTER 6: VERFICATION 56
6.1 Verification of Prediction Model 56
6.2 Data Split for Experiments 56
6.3 Results for RenRen’s Small World 56
6.3.1 Aggregate Experiment 56
6.3.2 Industry Performance 58
6.4 Comparing experiment results between Facebook and RenRen 62
CHAPTER 7: SOUNDNESS OF SOCIAL NETWORK FEATURES AS INVESTMENT BEHAVIOR INDICATORS 63
7.1 Soundness of Social Network Features as Investment Behavior Indicators 63
7.1.1 Performance between Datasets 63
7.1.2 Differences in Performance 63
Chapter 8: The Capital+ IT System 66
8.1 Architecture of Capital+ 66
8.2 Capital+ Walk Through 67
8.2.1 Exploring relationships 67
8.2.2 Recommended Investors and or Companies 71
8.2.3 Visualizing relationships between Investors and Companies 73
CHAPTER 9: CONCLUSION AND FUTURE WORK 75
9.1 Conclusion and Future Work 75
9.2 Summary of Contributions 75
9.2.1 Social Features are Reliable Features for Predicting Investment Behavior 75
9.2.2 Multiple Link Predictors Can Be Used to Gain Deeper and Broader Insight to the Network 76
9.2.3 Rules of thumb of when Investors will invest in Companies. 76
9.3 Vision for the future 77
9.3.1 Network Evolution of Investors 77
9.3.2 Application of Results to China’s Startup Environment 79
Reference 81

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