New Graduate Course
Stevens Institute of Technology
Approved by GCC 06-12-13
School:
Howe School of Technology Management
Course Title:
Social Network Analysis 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%): 12 summary reports addressing key concepts from class
Final paper (40%): A research proposal including literature review and preliminary
analyses
Credits:
3 credits
Other
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:
Class
Online
Modules
Other
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 department curriculum committee: 05-06-13
Sample Syllabus:
Topic(s)
Reading(s)
Week 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.
Week 4
Descriptive metrics of
entire networks
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.
HW
Burt, R. S. (1992). Structural holes: the social
structure of competition.
Week 5
Basic network models
Week 6
Graph algorithms
Week 7
Community detection
Week 8
Processes over
networks
Mark Granovetter (1983). The strength of weak
ties, a network theory revisited. Sociological
Theory, 1, 201-233.
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.
Wasserman, S., & Faust, K. (1994). Social
network analysis: Methods and applications.
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
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.
Week 9
Prediction on
networks
Week 10 Hard problems in
Social Network
Analysis
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.
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.
Borgatti, S. P. (2006). Identifying sets of key
players in a network. Computational,
Mathematical and Organizational Theory, 12, 2134.
Robins, G., Pattison, P., & Wang, P. (2006).
Closure, connectivity and degrees: New
Week 11 Dynamic Networks
specifications for exponential random graph (p*)
models for directed social networks
Leskovec, J., Backstrom, L., & Kumar, R. (2008).
Microscopic evolution of social networks.
Proceeding of the 14th
Week 12 Large-scale graph
Song, X., Lin, C., Tseng, B., & Sun, M. (2006).
Modeling Evolutionary Behaviors for
Community-based Dynamic Recommendation.
Proc. SIAM Intl. Conf. Data Mining.
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.
algorithms
Week 13 Defining the
boundaries of the
network model
Week 14 Final paper
presentations
Clauset, A., Newman, M., & Moore, C. (2004).
Finding community structure in very large
networks. Physical Review E, 70(6), 66111.
Tout, K., Evans, D. J. and Yakan, A. (2005).
Collaborative filtering: Special case in predictive
analysis. International Journal of Computer
Mathematics, 82, 1-11.