Syllabus

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). The course begins with a definition of a social network and a review of key concepts from the underlying mathematical field of graph theory. We then move on to dyadic concepts in network analysis, such as the notion of graph-theoretic distance. Next we cover node-level concepts, such as centrality and ego-network structure. Next we cover whole-network level concepts, such as network density. The end of the course is devoted to issues of research design and methodology, including data collection and analysis techniques.

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, including analyzing the data and writing up the results for a journal. The key deliverable for the course is a publishable research paper.

The textbook for this course is Wasserman and Faust, 1994 Social Network Analysis. Cambridge. This is an indispensable reference book, but difficult to read from front to back. Suggested readings from the book will be assigned, but there will be no required readings from it. All of the required readings are articles, chapters and handouts, which are given in the schedule. Links to all the readings can be found on the class website at www.analytictech.com/mgt780.

The final paper is due via email on May 4, by midnight. The schedule of classes can be found here:

Grading for this course is based on just two things: (1) a research paper (worth 75% of your grade), and (2) class participation (worth 25%). There are no exams in this course.

Research Paper (75%). For the paper, you must design and implement an empirical study of social networks. While you are not required to submit this paper to a journal for publication, it should be of publishable quality and written up in Academy of Management Journal format. Copies of past (successful) papers are available on the class website. The paper is due via email on the last day of class, April 30^th, before 2pm.

Class Participation (25%). I expect active participation in the classroom. My hope is that you will want to participate because we will be discussing interesting ideas. The abilities to interact with your colleagues effectively, to contribute to a group discussion, and to advocate an informed position are essential skills that will prepare you for the transition to a professional career. Your participation grade is based on your preparedness for class (e.g., having read the assigned reading), demonstration of a firm grasp of material covered, a willingness to seek clarification as appropriate, and the ability to integrate concepts and multiple perspectives. I will grade your participation according to the following criteria:

- the assessment provided by your fellow team members of your contribution to team assignments and discussions

The correspondence between letter grades and numerical percentages is as follows:

Letter	Percentage Range
A	90 – 100%
B	80 – 89%
C	70 – 79%
F	0 – 69%

As a PhD course, attendance is not strictly required, but it is expected (and necessary for a good participation grade). 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 (such as in-class exams and activities). 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.

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 455Y). Office hours are by appointment only, which you can arrange by email.