學術產出-Theses
Article View/Open
Publication Export
-
題名 投資者的社群行為
Social Behavior of Investors作者 梁煜銜
Liang, Yuxian Eugene貢獻者 苑守慈
Yuan, Soe Tysr
梁煜銜
Liang, Yuxian Eugene關鍵詞 社群網路分析
連節預測
機器學習
social network analysis
link prediction
machine learning日期 2013 上傳時間 10-Feb-2014 14:48:28 (UTC+8) 摘要 “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.參考文獻 1. Adamic, L. A and Ada, E.. “Friends and Neighbors on the Web”. Social Networks, Vol. 25, No. 3, pp 211-230, 20012. Backstrom, L., Boldi P., Rosa, M., Ugander, J., Vigna, S.. “ Four Degrees of Separation” Retrieved from http://arxiv.org/abs/1111.4570v3 on April 2011.3. Backstrom, L., Leskovec, J., “Supervised Random Walks: Predicting and Recommending Links in Social Networks”, ACM International Conference on Web Search and Data Mining (WSDM), Hong Kong, China, 20114. Bakker, L., Hare, W., Khosravi, H. and Ramadanovic, B. “A social network model of investment behavior in the stock market.” Physica A: Statistical Mechanics and its Applications, Vol. 389 No. 6, pp 1223-1229, 2010.5. Barnea, A., Cronqvist, H.and Siegel, S.. “Nature or Nurture: what determines investor behavior?”. Journal of Financial Economics, Vol. 98, No 3,, pp 583–604, 20106. Breiman, L., Friedman, J., Olshen, R., and Stone, C. “Classification and Regression Trees.” Wadsworth, Belmont, CA, 1984.7. Chang, C.C., and Lin, C.J., “LIBSVM: A Library for Support Vector Machines”, Department of Computer Science, National Taiwan University, Taipei, Taiwan. April 20128. Cortes, C., and Mohri, M., “AUC optimization vs. error rate minimization.” Proceedings of the Advances in Neural Information Processing Systems (NIPS’2003). British Columbia, Canada.9. Dean, J. and Ghemawat, S., “MapReduce: Simplified Data Processing on Large Clusters” OSDI 2004. Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, December 200410. Fire, M., Tenenboim, Lena., Lesser, O., Puzis, Rami., Rokach, L., and Elovici Y., “Link Prediction in Social Networks using Computationally Efficient Topological Features”, Third IEEE International Conference on Social Computing, SocialCom. MIT, Boston, USA, 201111. Freeman, L. C., “Centrality in Social Networks Conceptual Clarification”, Social Networks, Vol. 79, Vol. 1, No. 3, pp 215 – 239, 197912. Friedkin, N., “Horizon of Observability and Limits of Informal Control in Organizations”, Social Forces, Vol. 62, No. 1, pp 54-77, 198313. Gallagher, B., Tong H., Eliassi-Rad, T., and Faloutsos, C., “Using ghost edges for classification in sparsely labeled networks”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining Conference, Las Vegas, Nevada, USA, 2008 14. Ghemawat, S., Gobioff, H., and Lueng, S. T., “The Google File System”, 19th ACM Symposium on Operating Systems Principles, Lake George, NY, October 200315. Giot, P., Hege, U., Schwienbaher, A., “Expertise of Reputation? The Investment Behavior of Novice and Experienced Private Equity Funds.” 29th International Conference of the French Finance Association (AFFI), March 2012.16. Girvan, M., and Newman, M. E. J., “Community structure in social and biological networks”, PNAS, Volume 99, No. 12, 11, pp 7821-7826 , June 200217. Granovetter, M. S., “The Strength of Weak Ties”, American Journal of Sociology, Vol. 78, No. 6, May 197318. Grinblatt, M., and Keloharju, M., “The Investment Behavior and performance of various investor types: a study of Finland’s unique dataset.” Journal of Financial Economics, Vol. 55, No. 1, pp 43-67, January 2000.19. Hevener, A. R., March, S. T., and Park, J., “Design Science in Information Systems Research”, MIS Quarterly, Vol 28, No 1, pp 75-105, March 200420. Hwang, W., Kim, T., Ramanathan, M., and Zhang, A., “Bridging Centrality: Graph Mining from Element Level to Group Level”, Knowledge Discovery and Data Mining Conference, Las Vegas, Nevada, USA, 2008.21. James, S. D., David R. P., Wright, C., “Confidence opinions of market efficiency, and Investment Behavior of Finance Professors”, Journal of Financial Markets, Vol. 13, No. 1, pp 174-195, February 201022. Kajdanowicz, T., Kazienko, P., and Doskocz P., “Label-dependent feature extraction in social networks for node Classification”, Social Informatics: Second International Conference, SocInfo, Vol. 64, No. 30, pp 89-102, 201023. Kargar, M., and An A., “Discovering Top-k Teams of Experts with/without a Leader in Social Networks”, ACM Conference on Information and Knowledge Management, Glasgow, Scotland, UK, 2011.24. Kempe, D., Kleinberg, J., and Tardos, E., “Maximizing the spread of influence through a social network”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington DC. USA, 200325. Kleinberg J., “Authoritative Sources in a Hyperlinked Environment”, Proceedings of the ACM-SIAM Symposium on Discrete Algorithms. San Francisco, California. USA, 1998.26. Lappas, T., Liu, K., and Terzi, E., “Finding a Team of Experts in Social Networks”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining Conference Paris, France, 200927. Leskovec, J., Huttenlocher, D., Kleinberg, J., “Predicting Positive and Negative Links in Online Social Networks”. ACM WWW International Conference on World Wide Web (WWW), Raleigh, North Carolina, April 201028. Leskovec, J., Huttenlocher, D., Kleinberg Jon., “Signed Networks in Social Media” by. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), 2010. Atlanta, GA, USA.29. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., Briesen, Jeanne, V., and Glance, N., “Cost-effective outbreak detection in networks”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining Conference , San Jose, California, USA, 200730. Li, R. on “The Development of China’s Silicon Valley.”, Entrepreneurial Thought Leaders Lecture Series, September 23rd 2009. http://ecorner.stanford.edu/authorMaterialInfo.html?mid=2287. Retrieved on September 2012.31. Liang, Y. E., and Yuan, S. T., “The Social Behavior of Investors”, IEEE/ACM Advances in Social Network Analysis and Mining, Istanbul Turkey. August 201232. Liben-Nowell, D., and Kleinberg J., “The Link Prediction Problem for Social Networks.” Journal of the American Society for Information Science and Technology, Vol. 58, No. 7, pp 1019 - 1031, May 2007.33. Manning, C.D., Raghavan, P., and Schütze, H., “Introduction to Information Retrieval. Cambridge University Press”, pp. 234-265., 2008.34. McPherson M., Smith-Lovin L., Cook, J. M., “Birds of a Feather: Homophily in Social Networks”, Annual Review of Sociology, Vol. 27, pp 415-444, August 200135. Newman, M E. J., “The Structure of Collaborative Network.” www.pnas.orgycgiydoiy10.1073ypnas.021544898. Retrieved on March 2012.36. Newman, M. E. J., “Clustering and preferential attachment in growing networks”, Physical Review, Vol. 64, No. 2, April 200137. Newman, M. E. J., “The Structure and function of complex networks”, SIAM Review 45, pp 167- 256, 200338. Newman, M. E. J., “Modularity and community structure in networks”, PNAS, Volume 103, No. 23, pp 8577-8582, 6 June 200639. Page, L. and Brin, S., “The Anatomy of a large-scale hypertextual web search engine”, Proceedings of the Seventh International Conference on World Wide Web, Brisbane, Australia, 1998. 40. Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., and Barabasi, A. L., “Hierarchical Organization of Modularity in Metabolic Networks”, Science Magazine, Vol. 297, No. 5586, pp 1551-1555, 30 August 200241. Tan, W. K., Tan, Y. J. “An exploratory investigation of the investment information search behavior of individual domestic investors.” Telematics and Informatics, Vol. 29, No. 2, , pp 187-203, May 201242. Travers J., and Milgram S., “An experimental study of the small world problem”, Sociometry, Vol. 32, No 4, pp 425-443, December 196943. Tung. W. F., and Yuan, S. T., “Intelligent Service Machine”, Communications of the ACM, Vol.58, No.3, pp 129-134, 201044. Xiang, G., Zheng, Z., Wen, M., Hong, J., Rose, C., and Liu C., “A Supervised Approach to Predict Company Acquisition with Factual and Topic Features Using Profiles and News Articles on TechCrunch.” Sixth International AAAI Conference on Weblogs and Social Media, Trinity College, Dublin, Ireland, 2012 描述 碩士
國立政治大學
資訊管理研究所
100356020
102資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100356020 資料類型 thesis dc.contributor.advisor 苑守慈 zh_TW dc.contributor.advisor Yuan, Soe Tysr en_US dc.contributor.author (Authors) 梁煜銜 zh_TW dc.contributor.author (Authors) Liang, Yuxian Eugene en_US dc.creator (作者) 梁煜銜 zh_TW dc.creator (作者) Liang, Yuxian Eugene en_US dc.date (日期) 2013 en_US dc.date.accessioned 10-Feb-2014 14:48:28 (UTC+8) - dc.date.available 10-Feb-2014 14:48:28 (UTC+8) - dc.date.issued (上傳時間) 10-Feb-2014 14:48:28 (UTC+8) - dc.identifier (Other Identifiers) G0100356020 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/63652 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理研究所 zh_TW dc.description (描述) 100356020 zh_TW dc.description (描述) 102 zh_TW dc.description.abstract (摘要) “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。 zh_TW dc.description.abstract (摘要) “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. en_US dc.description.tableofcontents Table of ContentsCHAPTER 1: INTRODUCTION 11.1 Introduction 11.2 Contribution to Literature 21.2.1 Modeling prediction of investment behavior as a link prediction problem 21.2.2 Combining multiple link prediction techniques to gain greater insight of social networks 21.2.3 Providing general rules of thumb for companies seeking investment 31.3 Research Structure 3CHAPTER 2: RELATED WORK 72.1 Survey of Related Work 72.2 Related Research on Investment Behaviors 72.3 Related Research on Social Network Analysis 82.4 Other Related Research 92.5 Link Prediction as a Model to Predict Investor Behavior 9CHAPTER 3: PROSPERITY TAIWAN 113.1 Prosperity Taiwan Project Backgroud 113.2 Taiwan’s Economic Strengths and Current Economic Landscape 113.3 The “Prosperity” in Prosperity Taiwan 123.4 Vision of Prosperity Taiwan 123.5 Culture, Arts and Creativity as an Example 123.5 Intelligent Service Machines to aid Economic Transformation 133.5.1 The V+ Platform 14CHAPTER 4: METHOLOGY 174.1 Methodology 174.2 Dataset 204.2.1 CrunchBase Dataset 204.2.2 Data Selection 224.3 Concepts, Definitions and Examples 234.3.1 People 234.3.2 Companies 244.3.3 Financial Organization 244.3.4 Investors 244.3.5 Social Graph 244.3.5 Investment Graph 254.3.6 More definitions 264.4 Social Behavior of Investors in Facebook’s Small World 284.4.1 Understand Social Behavior using Descriptive Mining 294.4.2 Shortest Path 304.4.3 Adamic/Adar 334.4.3 Jaccard Coefficient 354.4.4 Common Neighbors 384.4.5 Preferential Attachment 394.4.6 Number of Shortest Paths between Investor and Company 404.4.7 Where’s the Money? Guidelines for Seeking Investments. 414.4.8 Summary of Intuition 414.5 Investors Are Social Animals: Modeling Investment Behavior as a Link Prediction Problem 414.5.1 Modeling Social Relationship 434.5.2 Learning Algorithms 434.5.3 Significance of Methodology 444.6 Experiment Setup 454.6.1 Evaluation Metrics 454.6.2 Evaluation 454.6.3 Cross Comparison of Performance Across Different Learning Algorithms 464.6.4 Ground Truth Labels 464.6.5 Data Split for Training and Testing 464.6.6 Experiment Runs 46CHAPTER 5: EXPERIMENTS 485.1 Experiment Result 485.1.1 Aggregate Performance 485.1.2 Industry Performance 505.1.3 Summary of Performance Categorical Performance 535.2 General Performance 535.2.1 Visualizing the Decision Process 54CHAPTER 6: VERFICATION 566.1 Verification of Prediction Model 566.2 Data Split for Experiments 566.3 Results for RenRen’s Small World 566.3.1 Aggregate Experiment 566.3.2 Industry Performance 586.4 Comparing experiment results between Facebook and RenRen 62CHAPTER 7: SOUNDNESS OF SOCIAL NETWORK FEATURES AS INVESTMENT BEHAVIOR INDICATORS 637.1 Soundness of Social Network Features as Investment Behavior Indicators 637.1.1 Performance between Datasets 637.1.2 Differences in Performance 63Chapter 8: The Capital+ IT System 668.1 Architecture of Capital+ 668.2 Capital+ Walk Through 678.2.1 Exploring relationships 678.2.2 Recommended Investors and or Companies 718.2.3 Visualizing relationships between Investors and Companies 73CHAPTER 9: CONCLUSION AND FUTURE WORK 759.1 Conclusion and Future Work 759.2 Summary of Contributions 759.2.1 Social Features are Reliable Features for Predicting Investment Behavior 759.2.2 Multiple Link Predictors Can Be Used to Gain Deeper and Broader Insight to the Network 769.2.3 Rules of thumb of when Investors will invest in Companies. 769.3 Vision for the future 779.3.1 Network Evolution of Investors 779.3.2 Application of Results to China’s Startup Environment 79Reference 81 zh_TW dc.format.extent 3326787 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100356020 en_US dc.subject (關鍵詞) 社群網路分析 zh_TW dc.subject (關鍵詞) 連節預測 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) social network analysis en_US dc.subject (關鍵詞) link prediction en_US dc.subject (關鍵詞) machine learning en_US dc.title (題名) 投資者的社群行為 zh_TW dc.title (題名) Social Behavior of Investors en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 1. Adamic, L. A and Ada, E.. “Friends and Neighbors on the Web”. Social Networks, Vol. 25, No. 3, pp 211-230, 20012. Backstrom, L., Boldi P., Rosa, M., Ugander, J., Vigna, S.. “ Four Degrees of Separation” Retrieved from http://arxiv.org/abs/1111.4570v3 on April 2011.3. Backstrom, L., Leskovec, J., “Supervised Random Walks: Predicting and Recommending Links in Social Networks”, ACM International Conference on Web Search and Data Mining (WSDM), Hong Kong, China, 20114. Bakker, L., Hare, W., Khosravi, H. and Ramadanovic, B. “A social network model of investment behavior in the stock market.” Physica A: Statistical Mechanics and its Applications, Vol. 389 No. 6, pp 1223-1229, 2010.5. Barnea, A., Cronqvist, H.and Siegel, S.. “Nature or Nurture: what determines investor behavior?”. Journal of Financial Economics, Vol. 98, No 3,, pp 583–604, 20106. Breiman, L., Friedman, J., Olshen, R., and Stone, C. “Classification and Regression Trees.” Wadsworth, Belmont, CA, 1984.7. Chang, C.C., and Lin, C.J., “LIBSVM: A Library for Support Vector Machines”, Department of Computer Science, National Taiwan University, Taipei, Taiwan. April 20128. Cortes, C., and Mohri, M., “AUC optimization vs. error rate minimization.” Proceedings of the Advances in Neural Information Processing Systems (NIPS’2003). British Columbia, Canada.9. Dean, J. and Ghemawat, S., “MapReduce: Simplified Data Processing on Large Clusters” OSDI 2004. Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, December 200410. Fire, M., Tenenboim, Lena., Lesser, O., Puzis, Rami., Rokach, L., and Elovici Y., “Link Prediction in Social Networks using Computationally Efficient Topological Features”, Third IEEE International Conference on Social Computing, SocialCom. MIT, Boston, USA, 201111. Freeman, L. C., “Centrality in Social Networks Conceptual Clarification”, Social Networks, Vol. 79, Vol. 1, No. 3, pp 215 – 239, 197912. Friedkin, N., “Horizon of Observability and Limits of Informal Control in Organizations”, Social Forces, Vol. 62, No. 1, pp 54-77, 198313. Gallagher, B., Tong H., Eliassi-Rad, T., and Faloutsos, C., “Using ghost edges for classification in sparsely labeled networks”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining Conference, Las Vegas, Nevada, USA, 2008 14. Ghemawat, S., Gobioff, H., and Lueng, S. T., “The Google File System”, 19th ACM Symposium on Operating Systems Principles, Lake George, NY, October 200315. Giot, P., Hege, U., Schwienbaher, A., “Expertise of Reputation? The Investment Behavior of Novice and Experienced Private Equity Funds.” 29th International Conference of the French Finance Association (AFFI), March 2012.16. Girvan, M., and Newman, M. E. J., “Community structure in social and biological networks”, PNAS, Volume 99, No. 12, 11, pp 7821-7826 , June 200217. Granovetter, M. S., “The Strength of Weak Ties”, American Journal of Sociology, Vol. 78, No. 6, May 197318. Grinblatt, M., and Keloharju, M., “The Investment Behavior and performance of various investor types: a study of Finland’s unique dataset.” Journal of Financial Economics, Vol. 55, No. 1, pp 43-67, January 2000.19. Hevener, A. R., March, S. T., and Park, J., “Design Science in Information Systems Research”, MIS Quarterly, Vol 28, No 1, pp 75-105, March 200420. Hwang, W., Kim, T., Ramanathan, M., and Zhang, A., “Bridging Centrality: Graph Mining from Element Level to Group Level”, Knowledge Discovery and Data Mining Conference, Las Vegas, Nevada, USA, 2008.21. James, S. D., David R. P., Wright, C., “Confidence opinions of market efficiency, and Investment Behavior of Finance Professors”, Journal of Financial Markets, Vol. 13, No. 1, pp 174-195, February 201022. Kajdanowicz, T., Kazienko, P., and Doskocz P., “Label-dependent feature extraction in social networks for node Classification”, Social Informatics: Second International Conference, SocInfo, Vol. 64, No. 30, pp 89-102, 201023. Kargar, M., and An A., “Discovering Top-k Teams of Experts with/without a Leader in Social Networks”, ACM Conference on Information and Knowledge Management, Glasgow, Scotland, UK, 2011.24. Kempe, D., Kleinberg, J., and Tardos, E., “Maximizing the spread of influence through a social network”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington DC. USA, 200325. Kleinberg J., “Authoritative Sources in a Hyperlinked Environment”, Proceedings of the ACM-SIAM Symposium on Discrete Algorithms. San Francisco, California. USA, 1998.26. Lappas, T., Liu, K., and Terzi, E., “Finding a Team of Experts in Social Networks”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining Conference Paris, France, 200927. Leskovec, J., Huttenlocher, D., Kleinberg, J., “Predicting Positive and Negative Links in Online Social Networks”. ACM WWW International Conference on World Wide Web (WWW), Raleigh, North Carolina, April 201028. Leskovec, J., Huttenlocher, D., Kleinberg Jon., “Signed Networks in Social Media” by. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), 2010. Atlanta, GA, USA.29. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., Briesen, Jeanne, V., and Glance, N., “Cost-effective outbreak detection in networks”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining Conference , San Jose, California, USA, 200730. Li, R. on “The Development of China’s Silicon Valley.”, Entrepreneurial Thought Leaders Lecture Series, September 23rd 2009. http://ecorner.stanford.edu/authorMaterialInfo.html?mid=2287. Retrieved on September 2012.31. Liang, Y. E., and Yuan, S. T., “The Social Behavior of Investors”, IEEE/ACM Advances in Social Network Analysis and Mining, Istanbul Turkey. August 201232. Liben-Nowell, D., and Kleinberg J., “The Link Prediction Problem for Social Networks.” Journal of the American Society for Information Science and Technology, Vol. 58, No. 7, pp 1019 - 1031, May 2007.33. Manning, C.D., Raghavan, P., and Schütze, H., “Introduction to Information Retrieval. Cambridge University Press”, pp. 234-265., 2008.34. McPherson M., Smith-Lovin L., Cook, J. M., “Birds of a Feather: Homophily in Social Networks”, Annual Review of Sociology, Vol. 27, pp 415-444, August 200135. Newman, M E. J., “The Structure of Collaborative Network.” www.pnas.orgycgiydoiy10.1073ypnas.021544898. Retrieved on March 2012.36. Newman, M. E. J., “Clustering and preferential attachment in growing networks”, Physical Review, Vol. 64, No. 2, April 200137. Newman, M. E. J., “The Structure and function of complex networks”, SIAM Review 45, pp 167- 256, 200338. Newman, M. E. J., “Modularity and community structure in networks”, PNAS, Volume 103, No. 23, pp 8577-8582, 6 June 200639. Page, L. and Brin, S., “The Anatomy of a large-scale hypertextual web search engine”, Proceedings of the Seventh International Conference on World Wide Web, Brisbane, Australia, 1998. 40. Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., and Barabasi, A. L., “Hierarchical Organization of Modularity in Metabolic Networks”, Science Magazine, Vol. 297, No. 5586, pp 1551-1555, 30 August 200241. Tan, W. K., Tan, Y. J. “An exploratory investigation of the investment information search behavior of individual domestic investors.” Telematics and Informatics, Vol. 29, No. 2, , pp 187-203, May 201242. Travers J., and Milgram S., “An experimental study of the small world problem”, Sociometry, Vol. 32, No 4, pp 425-443, December 196943. Tung. W. F., and Yuan, S. T., “Intelligent Service Machine”, Communications of the ACM, Vol.58, No.3, pp 129-134, 201044. Xiang, G., Zheng, Z., Wen, M., Hong, J., Rose, C., and Liu C., “A Supervised Approach to Predict Company Acquisition with Factual and Topic Features Using Profiles and News Articles on TechCrunch.” Sixth International AAAI Conference on Weblogs and Social Media, Trinity College, Dublin, Ireland, 2012 zh_TW