DSC 480 Social Network Analysis
Summary
Social Network Analysis is an exciting field that studies the impact of social networks on many areas including business, health, and the social sciences. When measuring the behavior of individuals using surveys and other data sources it is the attributes that are focussed on. Individuals are regressed, clustered, and measured by these attributes. Social Network Analysis instead focusses on the connections between individuals. Instead of clusters, networks of these connections are studied to understand the reasons why individuals connect.
Uses of social network analysis have helped prevent terrorism, predict viral messaging and diffusion, in marketing, for human analytics, and many other areas. This is a fast growing area that in previous years has been largely taught in PhD seminars. This class will emphasize the data science relationships and how it fits in with data centric approaches. The eventual goal is for the students to be able to gather social network data, process it, and develop predictions based on it. ERGM, which is the Social Network Analysis equivalent of regression, will be used as will other static network measures.
Texts
Required Resources:
1. [Easly] Easly, David & Kleinberg, Jon (2010) Networks, Crowds, and Marketing: Reasoning about a Highly Connected World, Cambridge University Press. ISBN 978-0-521-19533-1
2. [Cran] Cranmer, Skyler, Desmarais, Bruce A., & Morgan, Jason W. (2021) Inferential Network Analysis, Cambridge University Press. ISBN 978-1-316-61085-5
Recommended:
[Health] Valente, Thomas (2010) Social Networks and Health: Models, Methods,and Application. Oxford University Press. ISBN 978-0195301014
[R] Freeman, Michael & Ross, Joel (2018) Programming Skills for Data Science: Start writing code to wrangle, analyze, and visualize data with R, and GIT Addison-Wesley Data & Analytics Series. ISBN 978-0135133101
Grading
Grading is based:
50% Assessments on 2 projects for midterm and final
40% Assignments
10% Participation
In general work that is above expectations gets an A, work that meets expectations gets an A-, and work that has deficiencies gets B+ or below depending upon the level of deficiencies. If work is not turned in then this is graded to F. Work is expected to be on time, however if there is something preventing this contact me prior to the due date.
Prerequisites
An understanding of linear regression at the level of DSC 423, SOC 412, or PSY 411. Parts of the course will build upon these concepts. A basic familiarity with R is also needed. There are many resources for R that are free on the web and a book is recommended for the class. This is not a programming class, however loading files, putting them into data frames, adding libraries, running commands both interactive and through scripts will be needed. Other R commands that come from the Tidyverse will be introduced in class based on need.
Readings
[Easly] Chapter #2
Readings
[Easly] Chapter #4
Readings
[Easly] Chapter #3
Readings
[Easly] Chapter #5
Readings
[Easly] Chapter #18, #20
Readings
[Easly[ Chapter #19, #21
Readings
[Easly] Chapter #13, #14, #16
Readings
[Cran] Chapter #1, #2
[Cran] Chapter #3, #4
[Cran] Chapter #4, #5
This syllabus is subject to change as necessary during the quarter. If a change occurs, it will be thoroughly addressed during class, posted under Announcements in D2L and sent via email.
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