Course Syllabus
BIA658
Social Network Analytics
Fall, 2013
Instructor
Yasuaki Sakamoto , Assistant Professor
Office: Babbio 632
Office hours: By appointment
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
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 socialnetwork: friend, user-generated content, email, coauthor, affiliation
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.
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.
3
Graph visualization: nodes, edges, centrality, betweenness, reach, cliques, pathsBorgatti, 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. Visualize and statistically describe the network.
4
Network relationships: ties, social capital, structural holes,Mark Granovetter (1983). The strength of weak ties, a network theory
revisited. Sociological Theory, 1, 201-233.
Find relationships in the network.
structural balance
Burt, R. S. (1992). Structural holes: the social structure of competition.
5
Network structures:equivalence, small world, homophily, clustering, embeddedness
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. Find structures in the network.
6
Network evolution: random graphs, preferential attachment, reciprocityBarabási, 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. Fit models of growth to the network.
7
Diffusion in networks: information cascade, subgroups, prediction markets, votingWatts, 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. Simulate diffusion in the network.
8
Descriptive modeling: community/anomaly detection Fortunato, S. (2010). Community detection in graph. Physics Report, 486, 75-174.Identify different communities in the network.
Helbing, D. (2001). Traffic and related self-driven many-particle systems. Review of Modern Physics, 74, 1067-1141.
9
Predictive modeling:link/attribute prediction
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.
Predict future links in the network.
10
Marketing research: network data collection, sampling, hypothesis testing, research designBorgatti, S. P. (2006). Identifying sets of key players in a network.
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. Brainstorm about marketing application. Play with sampling.
11
Customer profiling: classification, predictive analysis using network dataYang, 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-million-person experiment in social influence and political mobilization.
Profile individuals in the network.
12
Trend: social influences on judgments, opinion spread, sentimentSalganik, 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. Analyze the sentiment of the network.
13
Network targeting: product diffusion,Van den Bulte, C., & Joshi, Y. V. (2007).
Build a
recommendation, segmentation, positioning
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.
14
Communicating results: presentation Integrate the results from the previous analyses and write a report. The final report will be delivered in the last week of the semester.