Unformatted text preview:

Ethical ConductCOURSE SCHEDULERevised: September 15, 2014Stevens Institute of TechnologyHowe School of Technology ManagementSyllabusBIA 658 Social Network AnalyticsFall 2014 Mondays 6:15-8:45PM, EAS 231Hani SafadiOffice: Babbio 630Tel: 201-216-3391Fax: [email protected] Hours: Mon 4:30-5:30PM and Tue 3:30-4:30PMAlso by appointmentCourse Room/Web Address: http://www.stevens.edu/moodleOverviewThis 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.Prerequisites: Admission requirements for the BI&A program.Course ObjectivesIn this course, students will learn how to analyze social network data and apply the analyses in various business and real life settings. The course focuses on network concepts, including graph-theoretic fundamentals, centrality, cohesion, affiliations, equivalence, and roles, as well as design issues, including data sampling and hypothesis testing. Theoretical areas covered include embeddedness, social capital, homophily, and network growth. Another focus of this course is on marketing applications of social network analysis, in particular the use of knowledge about network properties and behavior, such as hubs and paths, the robustness of the network, and information cascades, to better broadcast products and search targets After taking this course, studentsshould be able to statistically analyze and describe large scale networks, model the evolution of networks, and apply the network analyses in business settings.Additional learning objectives include the development of:Written and oral communications skills: students will write a project report and present their projects at the end of the course.Team skills: The final project for the course will involve student teams; an online survey instrument will be used to measure individual contributions to team performance. 1List of Course Outcomes: After taking this course, students will be able to:- 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.- 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.PedagogyThe course will employ lectures, class discussion, in-class individual assignments, individual homeworks and a team project. In the team project, students will analyze a realindustrial problem, formulate a model, collect data, solve the problem using one or more of the techniques discussed in class, and interpret the solution for management.2ReadingsRequired Text - Golbeck, Jennifer. Analyzing the social web. Newnes, 2013.o http://booksite.elsevier.com/9780124055315/ o http://analyzingthesocialweb.com - Derek Hansen, Ben Shneiderman, and Marc A. Smith. Analyzing social media networks with NodeXL: Insights from a connected world. Morgan Kaufmann, 2010.o https://nodexl.codeplex.com/wikipage?title=NodeXL%20Teaching%20Resources Extra ArticlesTopic ArticlesBasic Concepts- 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, 414-416NodeXL & NetworkX- Hagberg, A., Swart, P., & S Chult, D. (2008). Exploring network structure, dynamics, and function using NetworkX (No. LA-UR-08-05495; LA-UR-08-5495). Los Alamos National Laboratory (LANL)- Smith, M. A., Shneiderman, B., Milic-Frayling, N., Mendes Rodrigues, E., Barash, V., Dunne, C., ... & Gleave, E. (2009, June).Analyzing (social media) networks with NodeXL. In Proceedings of the fourth international conference on Communities and technologies (pp. 255-264). ACM.Structure and Measures- Brandes, U., & Pich, C. (2007). Centrality estimation in large networks. International Journal of Bifurcation and Chaos, 17(07).- Borgatti, S. P., & Halgin, D. S. (2011). Analyzing affiliation networks. The Sage Handbook of Social Network Analysis, 417–433.Sociological Concepts- Mark Granovetter (1983). The strength of weak ties, a network theory revisited. Sociological Theory, 1, 201-233- Burt, R. S. (1992). Structural holes: the social structure of competitionAttributes andContent- Yang, T., Jin, R., Chi, Y., & Zhu, S. (2009, June). Combining link and content for community detection: a discriminative approach. InProceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 927-936). ACM.Groups and Communities- Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821-7826.- Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National 3Academy of Sciences, 99(12), 7821-7826- Fortunato, S. (2010). Community detection in graph. Physics Report, 486, 75-174- Yang, J., & Leskovec, J. (2012, December). Community-affiliationgraph model for overlapping network community detection. In Data Mining (ICDM), 2012 IEEE 12th International Conference on (pp. 1170-1175)Formation and Evolution- Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509-512- Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipf's law, M. E. J. Newman, Contemporary Physics 46, 323-351- Jackson, M. O., & Rogers, B. W. (2007). Meeting Strangers and Friends of Friends: How Random Are Social Networks? American Economic Review, 97(3), 890–915Contagion and Propagation- Sinan Aral, Lev Muchnik, and Arun Sundararajan (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. PNAS, 106, 21544-21549- Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34, 441-458- Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106(51), 21544-21549.Link Prediction- Liben-Nowell, D. and Kleinberg, J. (2007), The link-prediction problem for social networks. Journal of the American Society for Information Science and

View Full Document

STEVENS BIA 658 - Syllabus

Download Syllabus
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...

Join to view Syllabus and access 3M+ class-specific study document.

We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Syllabus 2 2 and access 3M+ class-specific study document.


By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?