New Graduate Course
Stevens Institute of Technology
Approved by GCC 06-12-13
School:Howe School of Technology Management
Course Title: Social NetworkAnalysis Research Seminar
Program: Howe School Ph.D. Program
Course: MGT786
Catalog Description:
This course addresses concepts and theories of social networks and social network analysis.Core concepts include representations and models of networks, basic descriptive statistics at the individual and network level, and standard models of network formation. The course also covers more advanced topics in network theory, including community detection, processes over networks such as contagion and influence, and models of dynamic networks.
Course Objectives:
Over the past decade, awareness of the extent to which people are connected and how they are connected has skyrocketed, largely due to modern information system technologies such as the world wide web and social networking sites such as Facebook and Twitter. Thus, thorough knowledge of modern information systems technologies requires a deep understanding of social networks and social network analytic techniques. This course provides students with the knowledge and tools required to leverage these techniques.
List of Course Outcomes:
After taking this course, students will:
-Have a thorough understanding of networks of all types
-Be well-versed in the literature on social networks and social networks analysis
-Write code to analyze and visualize simple networks
-Have a deep understanding of models of processes on networks
-Be able to model the evolution of social networks
Prerequisites:Only for accepted Ph.D. students
Cross-listing: None
Grading Percentages: HW Class work Mid-term Final Projects
Other
Class work (20%): Participation
HW (40%):12summary reports addressing key concepts from class
Final paper (40%): A research proposal including literature review and preliminary analyses
Credits: 3 creditsOther
For Graduate Credit toward Degree or Certificate:
Yes No Not for Dept. Majors Other
Textbook(s) or References:(List required and recommended texts including publisher and year in a recognized format such as APA, AIP, Chicago or MLA):
See Readings in the sample syllabus.
Mode of Delivery:ClassOnlineModulesOther
Program/Department Ownership:Information Systems
When first offered: Fall 2014
Department Point of Contact and Title:Winter Mason, Assistant Professor
Date approved by individual school and/or departmentcurriculum committee:05-06-13
Sample Syllabus:
Topic(s) / Reading(s) / HWWeek 1 / Introduction to course
Basic Network Concepts / Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications.
Week 2 / Types of networks
Tools for visualization / Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications.
Week 3 / Descriptive metrics of ego networks / Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications.
Borgatti, S. P. (2005). Centrality and network flow, Social Networks, 27, 55-71.
Week 4 / Descriptive metrics of entire networks / Albert, R., Jeong, H., and Barabási, A.-L. (1999). Diameter of the WORLD-Wide
Web, Nature, 401, 130-131.
Burt, R. S. (1992). Structural holes: the social structure of competition.
Mark Granovetter (1983). The strength of weak ties, anetwork theory revisited. Sociological Theory, 1, 201-233.
Week 5 / Basic network models / 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.
Week 6 / Graph algorithms / Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications.
Week 7 / Community detection / Newman, M. E. J. (2004). Fast algorithm for detecting community structure in networks, M. E. J. Newman, Phys. Rev. E 69, 066133.
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75–174. doi:10.1016/j.physrep.2009.11.002
Week 8 / Processes over networks / 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 (Vol. 106, pp. 21544–21549). Proceedings of the National Academy of Sciences.
Shalizi, C. R., & Thomas, A. C. (n.d.). Homophily and Contagion are generically confounded in observational social network studies. Sociological Methods & Research, 40(2), 211–239.
Week 9 / Prediction on networks / 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.
Hill, S., Provost, F., & Volinsky, C. (2006). Network-Based Marketing: Identifying Likely Adopters via Consumer Networks. Statistical Science, 21, 256-276.
Week 10 / Hard problems in Social Network Analysis / Borgatti, S. P. (2006). Identifying sets of key players in a network. Computational, Mathematical and Organizational Theory, 12, 21-34.
Robins, G., Pattison, P., & Wang, P. (2006). Closure, connectivity and degrees: New specifications for exponential random graph (p*) models for directed social networks
Week 11 / Dynamic Networks / Leskovec, J., Backstrom, L., & Kumar, R. (2008). Microscopic evolution of social networks. Proceeding of the 14th
Song, X., Lin, C., Tseng, B., & Sun, M. (2006). Modeling Evolutionary Behaviors for Community-based Dynamic Recommendation. Proc. SIAM Intl. Conf. Data Mining.
Week 12 / Large-scale graph algorithms / Bekkerman, Ron, Mikhail Bilenko, and John Langford. "Scaling up machine learning: parallel and distributed approaches."Proceedings of the 17th ACM SIGKDD International Conference Tutorials. ACM, 2011.
Clauset, A., Newman, M., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6), 66111.
Week 13 / Defining the boundaries of the network model / Tout, K., Evans, D. J. and Yakan, A. (2005). Collaborative filtering: Special case in predictive analysis. International Journal of Computer Mathematics,82, 1-11.
Week 14 / Final paper presentations