Course Syllabus BIA658 Social Network Analytics Fall, 2013 Instructor Yasuaki Sakamoto, Assistant Professor Office: Babbio 632 Office hours: By appointment ysakamot@stevens.edu Course Description This course introduces concepts and theories of social network and social media analyses. Application areas include customer profiling, community and trend detection, targeting, sentiment analysis, and development of recommendation systems. Course Objectives In this course, students will: master theories of social networks and social behavior - acquire techniques for analyzing social network data; apply analytical skills to social network data ; apply social network analysis to marketing research Course Outcomes After taking this course, students will be able to: statistically analyze social networks ; model the evolution of social networks ; describe network properties - predict network behavior ; help develop marketing strategies based on social network analysis Course Materials We will use articles as reading materials. These articles will be available online (http://personal.stevens.edu/~ysakamot/bia658/). I recommend the following books: Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Easley, D., & Kleinberg, J. (2010). Networks, crowds, and Markets; Reasoning about a highly connected world.
Grading Here is the breakdown for the grading purposes: - Participation 20% - Assignments 30% - Project 50% Ethical Conduct The following statement is printed in the Stevens Graduate Catalog and applies to all students taking Stevens courses, on and off campus. "Cheating during in-class tests or take-home examinations or homework is, of course, illegal and immoral. A Graduate Academic Evaluation Board exists to investigate academic improprieties, conduct hearings, and determine any necessary actions. The term 'academic impropriety' is meant to include, but is not limited to, cheating on homework, during in-class or take home examinations and plagiarism." Reference: The Graduate Student Handbook, Academic Year 2003-2004 Stevens Institute of Technology, page 10. Consequences of academic impropriety are severe, ranging from receiving an "F" in a course, to a warning from the Dean of the Graduate School, which becomes a part of the permanent student record, to expulsion. Consistent with the above statements, all homework exercises, tests and exams that are designated as individual assignments MUST contain the following signed statement before they can be accepted for grading: I pledge on my honor that I have not given or received any unauthorized assistance on this assignment/examination. I further pledge that I have not copied any material from a book, article, the Internet or any other source except where I have expressly cited the source. Signature Date Please note that assignments in this class may be submitted to www.turnitin.com, a web-based anti-plagiarism system, for an evaluation of their originality. Course/Teacher Evaluation Continuous improvement can only occur with feedback based on comprehensive
and appropriate surveys. Your feedback is an important contributor to decisions to modify course content/pedagogy which is why we strive for 100% class participation in the survey. All course teacher evaluations are conducted on-line. You will receive an e-mail one week prior to the end of the course informing you that the survey site (https://www.stevens.edu/assess) is open along with instructions for accessing the site. Login using your Campus Pipeline (email) 'CPIPE' username and password. This is the same username and password you use for WebCT. Simply click on the course that you wish to evaluate and enter the information. All responses are strictly anonymous. We especially encourage you to clarify your position on any of the questions and give explicit feedbacks on your overall evaluations in the section at the end of the formal survey which allows for written comments. We ask that you submit your survey prior to the last class. Course Schedule Course schedule will be posted online (http://personal.stevens.edu/~ysakamot/bia658/). Make sure to regularly consult the web page for an updated schedule. Week Topic(s) Reading(s) HW 1 Introduction: overview and goals 2 Types of social network: friend, user-generated content, email, coauthor, affiliation 3 Graph visualization: nodes, edges, centrality, betweenness, reach, cliques, paths 4 Network relationships: ties, social capital, structural holes, Borgatti S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network Analysis in the Social Sciences, 323, 892-895. Butts, C. T. (2009). Revisiting the Foundations of Network Analysis, 325, 414-416. Hill, S., Provost, F., & Volinsky, C. (2006). Network-Based Marketing: Identifying Likely Adopters via Consumer Networks. Statistical Science, 21, 256-276. Borgatti, S. P. (2005). Centrality and network flow, Social Networks, 27, 55-71. Albert, R., Jeong, H., and Barabási, A.- L. (1999). Diameter of the WORLD- Wide Web, Nature, 401, 130-131. Mark Granovetter (1983). The strength of weak ties, a network theory revisited. Sociological Theory, 1, 201-233. Find a social network to analyze in this course, with marketing application in mind. Working students can analyze the social networks in their own companies. Visualize and statistically describe the Find relationships in the
structural balance 5 Network structures: equivalence, small world, homophily, clustering, embeddedness 6 Network evolution: random graphs, preferential attachment, reciprocity 7 Diffusion in networks: information cascade, subgroups, prediction markets, voting 8 Descriptive modeling: community/anomaly detection Burt, R. S. (1992). Structural holes: the social structure of competition. Hornsey, M. J., & Hogg, M. A., (2000). Intergroup similarity and subgroup relations: Some implications for assimilation. Personality and Social Psychology Bulletin, 26, 948-958. Flynn, F. J., Reagans, R. E., & Guillory, L. (2010). Do you two know each other? Transitivity, homophily, and the need for (network) closure. Journal of Personality and Social Psychology, 99, 855-869 Sinan Aral, Lev Muchnik, and Arun Sundararajan (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. PNAS, 106, 21544-21549. Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509-512. Newman, M. E. J., Watts, D. J., & Strogatz, S. H., (2002). Random graph models of social networks. Proceedings of the National Academy of Sciences, 99, 2566-2572. Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipf s law, M. E. J. Newman, Contemporary Physics 46, 323-351. Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34, 441-458. Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. The Journal of Political Economy, 100, 992-1026. Sinan Aral and Dylan Walker (2012). Identifying Influential and Susceptible Members of Social Networks. Science, 337, 337-341. Fortunato, S. (2010). Community detection in graph. Physics Report, 486, 75-174. Find structures in the Fit models of growth to the Simulate diffusion in the Identify different communities in the
9 Predictive modeling: link/attribute prediction 10 Marketing research: network data collection, sampling, hypothesis testing, research design 11 Customer profiling: classification, predictive analysis using network data 12 Trend: social influences on judgments, opinion spread, sentiment 13 Network targeting: product diffusion, Helbing, D. (2001). Traffic and related self-driven many-particle systems. Review of Modern Physics, 74, 1067-1141. Liben-Nowell, D. and Kleinberg, J. (2007), The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58: 1019 1031. Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47-97. Borgatti, S. P. (2006). Identifying sets of key players in a Computational, Mathematical and Organizational Theory, 12, 21-34. Salganik, M. J., & Watts, D. J. (2009). Web-based experiments for the study of collective social dynamics in cultural markets. Topics in Cognitive Science, 1, 439-468. Yang, S. and Allenby, G. M. (2003). Modeling interdependent consumer preferences. Journal of Marketing Research, 40, 282-294. Bond, R. M. et al. (2012). A 61-millionperson experiment in social influence and political mobilization. Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311, 854-856. Salganik, M. J., & Watts, D. J. (2008). Leading the herd astray: An experimental study of self-fulfilling prophecies in an artificial cultural market. Asur, S., & Huberman, B. A.. Predicting the future with social media. Cialdini, R. B., & Sagarin, B. J. (2005). Principles of Interpersonal Influence. In Brock, T. C. and Green, M. C. (Eds.) Persuasion: Psychological insights and perspectives. Van den Bulte, C., & Joshi, Y. V. (2007). Predict future links in the Brainstorm about marketing application. Play with sampling. Profile individuals in the Analyze the sentiment of the Build a recommendation
recommendation, segmentation, positioning 14 Communicating results: presentation New product diffusion with influentials and imitators. Marketing Science, 26, 400-421 Fildes, R. (2003). Review of New- Product Diffusion Models, by V. Mahajan, E. Muller and Y. Wind, eds. International Journal of Forecasting, 19, 327-328. Peres, R., Muller, E., & Mahajan, V. (2012). Innovation diffusion and new product growth models: A critical review and research directions. Aral, S. (2010). Commentary: Identifying Social Influence: A Comment on Opinion Leadership and Social Contagion in New Product Diffusion. Marketing Science, 1-7. algorithm based on network analysis. Integrate the results from the previous analyses and write a report. The final report will be delivered in the last week of the semester.