Welcome to 08-201:
Introduction to Social Network Analysis

Institute for Software Research, Carnegie Mellon University

Taught in Spring 2010 by:

Class Time: Monday & Wednesday, 1:30-2:20 pm

Location: Wean Hall 5312

Course Units: 9

Office hours:

Syllabus (most current schedule)

Course Description

Who is key in a group? How fast can a message spread on Facebook? Are you really six degrees away from a random stranger? Learn how to answers these questions in 08201.

Social Network Analysis (SNA) has become a widely applied method in research and business for inquiring the web of relationships on the individual, organizational and societal level. With ready access to computing power, the popularity of social networking websites such as Facebook, and automated data collection techniques the demand for solid expertise in SNA has recently exploded. In this course, students learn how to conduct SNA projects and how to approach SNA with theoretic, methodological, and computational rigor.

This interdisciplinary, undergraduate-level course introduces students to the basic concepts and analysis techniques in SNA. Students learn how to identify key individuals and groups in social systems, to detect and generate fundamental network structures, and to model growth and diffusion processes in networks. Students will be trained in interpreting the meaning of the aforementioned phenomena and suggesting potential courses of action to reinforce or change the observed trends. After this course, students will be able to design and execute network analysis projects including collecting data and considering ethical and legal implications, to perform systematic and informed analyses of network data for personal, commercial and scholarly use, and to critically review SNA projects conducted by others.

Learning Objectives

The main learning objective with this course is to enable students to put Social Network Analysis projects into action in a planned, informed and efficient manner. This overarching goal involves the following subtasks:

  • Formalize different types of entities and relationships as nodes and edges and represent this information as relational data.
  • Plan and execute network analytical computations.
  • Use advanced network analysis software to generate visualizations and perform empirical investigations of network data. 
  • Interpret and synthesize the meaning of the results with respect to a question, goal, or task.
  • Collect network data in different ways and from different sources while adhering to legal standards and ethics standards.

This is an interdisciplinary course designed to benefit from a broad representation of students from different colleges and programs. No specific technical or numerical background is required, but students are expected to be willing to hone their computational skills. See the instructor if you have any concerns about your preparedness for this course.

Course Organization

The social network analysis process involves four basic steps as shown in the graph on below:

  • Define a goal, question or task.
  • Collect data.
  • Analyze the data.
  • Interpret the results in order to complete your goal, answer your question, or solve your task.


In the first half of the course students will acquire the knowledge and skills needed in order to handle steps 1., 3., and 4.. In this part of the course, students learn how to investigate networks from the general to the specific, i.e. from the graph level over groups and dyads to individual nodes. For each of these levels, we will examine the observed structure by using different methods and we will interpret the meaning of the observations. Each of these levels will have a homework assignment associated with it that will be given towards the end of the section and will be due a week later.

The second half of the course serves two purposes:
First, we delve into the area of network data collection. Students will be trained in different ways of acquiring network data, including surveys, text mining, and simulations. They will also learn about the legal and ethical constraints associated with various data sources and collection techniques. This part of the course involves two homeworks.
Second, the students will put the knowledge that they acquired in part one of the course into action by planning and executing a small-scale network analysis project. The project is associated with three home work deliverables, including an in-class presentation of each team’s  study. This final presentation is the substitute for a final exam.
The class meeting time will be centered on lecture, but will also include a substantial amount of class discussion at times.

Audit Policy

You are welcome to audit our course. We do ask you to fill out an audit form (available from the HUB webpage) and get it signed by us.


Software:  The AutoMap and ORA software will be used through the semester. Both tools are freely available from  www.casos.cs.cmu.edu Note: these software products are windows-only. They will be installed in the clusters.


  • Textbooks (Required): 
    • Scott, J. (2007). Social network analysis: A handbook (2nd Ed.).Newbury Park, CA: Sage.
    • Knoke (2008). Social Network Analysis, (2nd Ed).Sage.
    • both books are on hold for this class at CMU's Engineering & Science Library at 4400 Wean Hall and are available at the CMU bookstore
  • Other readings (required and optional) will be provided.


Beyond submitting the deliverables, students will benefit greatly from the course if they participate in class discussions and discuss the topics with other students outside of class. This is a fun topic with an incredible amount of real-life application both personally and professionally no matter what life-course one takes after the semester. Social Network Analysis is still a relatively new field, so many ideas are yet unexplored. We encourage the attendees to approach this course as one that desires hard work, but to also bring an attitude of having fun. The instructors will do all they are capable of to make this an intellectually rewarding course with a good dose of fun!