Welcome to Introduction to Social Networks (17-210)
Course Description
What makes a recommendation system on social media effective? Why do YouTube mega-influencers with tens of millions of subscribers exist, yet we have not heard of most of them? Why do echo chambers form and polarization deepen in social media platforms despite the utopian promise of the free flow of information and fluid connectivity between people that these platforms had envisioned? How do professionals land their dream jobs? How does mass adoption of technological innovations happen (e.g., Python, Bitcoin)? Underlying these seemingly unrelated questions is the powerful influence of social networks, the collection of social connections that people form on and offline.
This course offers an introduction to the study of social networks as a powerful tool for understanding a wide range of questions and for solving real-world problems arising at the intersection of human social behavior and technological systems. The course first introduces network concepts and their mathematical operationalizations that give social network analysis the versatility for rigorous quantitative analysis. Students will subsequently learn how these building blocks have been applied to analyze and solve a wide range of online social phenomena. Finally, the course will introduce statistical models of networks that enable principled investigation of network formation mechanisms.
Throughout this course, students will work towards a final team project that applies network concepts and measures to either (a) describe and/or explain an empirically puzzling social phenomenon with network data, (b) develop a network data collection pipeline that aims to solve a real-world problem, or (c) build a system utiziling the insights from the course that solves a real-world problem. The following class activities throughout the course are intended to support and augment the final project.
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.
Homework 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 later in the semester project progress memos that summarize team activities, progress, and challenges related to the final project.
Midterm exam (open book).
Final team project. The team project will be a research paper that describes and/or explains an interesting problem or a technological artifact using the tools of social network analysis on a field/domain that the team members collectively choose. The team will present the results at the last day of the meeting and submit a final report. There will be no final exam.
Fall 2022 class will meet on Tuesdays and Thursdays 1:25pm~2:45pm at Wean Hall 3203.
Who Is This Course For?
CMU undergraduate students who are…
- interested in phenomena that occur on networks of people, from social contagion, online activism, open-source collaboration, and cryptocurrency transactions.
- seeking insight and understanding on how networks form the way they do.
- interested in building a solid social science knowledge foundation for understanding features engineered for machine learning on graphs and for developing intuition for the interpretation of black box models (e.g., graph representation learning).
Learning Goals
- Understand how relationships and interactions between people can be conceptualized as networks.
- Understand the basic concepts and measures in social network analysis.
- Become proficient in choosing and using the adequate computational tools for network analysis.
- Apply the concepts, measures, and tools towards developing research questions and solving them using empirical network data.