About the Course
Table of contents
Instructor
- Patrick Park (patpark@cmu.edu)
- TCS 324
Time
- Fall 2022
- Tuesday, Thursday 1:25PM-2:45PM (Room TBD)
- No recitation
Course Overview
This course offers an introduction to social networks, a powerful analytical lens for describing and understanding the actions of individuals, interactions between individuals, and the structure of social groups in today’s online socio-technical systems.
The course first builds a solid foundational understanding of the concepts that capture robust characteristics of human social behavior and how those concepts are translated into mathematical constructs and measures that can be used for rigorous quantitative analysis.
Students will learn how these measures have been applied to analyze a wide range of social phenomena, such as the diffusion of behaviors and attitudes in social media platforms, online polarization and the social underpinnings of echo chambers, the emergence of hierarchy and inequality in exchange systems such as labor markets and cryptocurrency transactions, and collaborative problem-solving in scientific knowledge production.
Students will work towards a final social network analysis project that describes and/or solves an empirically puzzling social phenomenon through the application of network concepts and measures in the analysis and interpretation of network data. The following class activities are intended to support and augment the research report.
Lectures
Lectures, delivered by the instructor, will cover the concepts, methods, and application of social network analysis. Part of the lecture will also be used in relation to the assignments, such as Q&A, discussions of the assigned readings, etc.
Assignments
Throughout the semester, you will be assigned weekly memos that summarize the main ideas of the readings. In addition, you will submit weekly assignments designed to build familiarity with the software and analysis techniques discussed in the lectures. These may also include a project progress memo that describes the team activities, progress, and challenges related to the team project.
Evaluation and Grading
There will be a midterm exam and a final team project. The team project will be a research paper that either describes or explains an interesting problem using the tools of social network analysis on a field/domain that the team members choose. The team will present the results at the last day of the meeting and submit a final report. There will be no separate final exam.
The overall grading will be based on the following criteria, subject to change.
- Class participation 10%
- Homework Assignments 20%
- Midterm exam 30%
- Final project 40%
Prerequisite Knowledge
Working knowledge of linear algebra and statistics is preferrable. Given the diverse academic backgrounds, interests, and technical knowledge of participating students, no specific technical background is required, but students are strongly expected to be self-motivated in understanding how the mathematic and quantitative operationalizations capture the social scientific insights behind them and in diving into learning network analytic software/libraries.