Syllabus

COURSE DESCRIPTION

This is a PhD level course on social network analysis. The focus is both theoretical (e.g., what are the key concept of social network analysis) and methodological (e.g., how do we actually carry out research on social networks). What the course is not is a survey of social network research to date (we have a separate course for that taught by Prof. Dan Brass, typically in the Fall). Above all, the course is about network concepts in the context of actually doing research. 

The concepts covered include basic graph theory, cohesion, subgroups, centrality, structure and position, modeling change, detecting vulnerabilities, and more. 

GOALS AND OUTCOMES

This is a hands-on course with the objective of teaching a student how to do a network analysis. At the end of this course, a student should be able design and implement a social network analysis research project. 

The deliverables for this course consist of: 

• A hands-on final exam (70%)

• Class participation in general (30%)

In addition, I will assign homework exercises.

SCHEDULE OF ASSIGNMENTS

• See the Schedule page

MATERIALS

The textbook (referred to as ASN in the schedule) for this course is:

• Borgatti, Everett and Johnson (2024).  Analyzing Social Networks. 3rd edition. Sage. 

In addition, you will need the following software package:

• Borgatti, S.P., Everett, M.G. and Freeman, L.C. 2002. Ucinet 6 for Windows:  Software for Social Network Analysis. Harvard, MA: Analytic Technologies.

You will be provided with a free registration code in class, which is good for the life of the product. For more information on downloading and installing the software, please see the Software page. 

EMAIL ANNOUNCEMENTS

I use an email list called https://groups.io/g/MGT780 to communicate with the class. Make sure you sign up for it.

FORMAT

Some classes are entirely about discussing readings. Note that each student is responsible for all of the readings, but for each reading one person will be asked to present the article to the class. Please decide among yourselves who will present which reading.

Other classes consist of using the software to analyze data.

COURSE POLICIES

As a PhD course, attendance is not strictly required, but is expected. I would appreciate being notified ahead of time if you are not going to be attending any particular class.

You have a responsibility to maintain the highest standards of academic integrity in both individual and group work, and to comply with the University of Kentucky policy on academic integrity. Any instances of cheating or plagiarism will be subject to the disciplinary procedures of the University. Please speak to me if you have any questions about academic integrity or concerns about any classmate’s behavior. Please bring any ethical questions or concerns to me before submitting an assignment or participating in an activity. Two general rules of thumb: When in doubt about using material, make sure you cite it. When in doubt about collaborating, sharing, etc., don’t do it without checking with me.

If you have a documented disability that requires academic accommodations, please see me as soon as possible during scheduled office hours. In order to receive accommodations in this course, you must provide me with a Letter of Accommodation from the Disability Resource Center (Room 2, Alumni Gym, 257‐2754, email address jkarnes@email.uky.edu) for coordination of campus disability services available to students with disabilities.

CONTACT

The preferred way to contact me is via email (sborgatti@uky.edu). You can also try my office phone (257-2257) or just drop by my office (B&E 323W). You can also arrange an appointment to meet -- just send me an email.