Class meetings: Mondays, 12-2pm (458 Evans Hall)
Instructor: Dennis M. Feehan ([email protected])
Office hours: Mondays, 3-4pm (2232 Piedmont Ave, Rm 210), or by appointment
NOTE: there will be a different office hours schedule for the first two weeks of classes; we will discuss this in the first class
Course control number: 46819
In this connector, we will explore the science of social networks. This interdisciplinary subject focuses on measuring, modeling, and understanding the different types of connections and interactions between people. Social networks come in many different types and sizes: there are small, tightly-knit networks like the members of a family; and there are also massive, loosely connected networks like the users of Twitter.
Insights from the study of social networks are used in a wide range of different real-world settings. For example, demographers and epidemiologists at the Centers for Disease Control and UNAIDS use network models to help them predict and prevent the spread of infectious diseases like HIV and Ebola; data scientists at Facebook and Google use ideas from social networks to build products that enable people all across the globe to connect with one another; and researchers working on political campaigns use insights from social networks to try and convince people to turn out and vote for their candidate on election day.
Studying social networks means working with a broad toolkit of theories and methods drawn from the social, natural, and mathematical sciences. In this connector class, we will explore a few key ideas from this toolkit. Our goal will be to understand what a social network is, to learn how to work with social network data, and to illustrate some of the ways that understanding social networks can be useful in theory and in practice.
See a description of the work required for the course here.
NB: This syllabus is approximate, and will be adjusted as the semester progresses.
| Date | Topic | Lab | Homework | Reading |
|---|---|---|---|---|
| 8/28 | Introduction and course overview | Hwk 01 (due 9/7) | ||
| 9/4 | LABOR DAY (no class) | |||
| 9/11 | Personal networks | Lab 01 | ||
| 9/18 | Homophily and personal networks | Lab 02 | Hwk 02 (due 9/27) | |
| 9/25 | Intro to working with complete network data | Lab 03 | ||
| 10/2 | Quantifying network structure: components, degree distributions, and path lengths | Lab 04 | Hwk 03 (due 10/11) | Friends you can count on |
| 10/9 | The Erdos-Renyi random network model | Lab 05 | ||
| 10/16 | Clustering in social networks | Lab 06 | Hwk 04 (due 10/25) | |
| 10/23 | Attributes and assortativity | Lab 07 | ||
| 10/30 | Dynamics and centrality | Lab 08 | Hwk 05 (due 11/8) | |
| 11/6 | Affiliation networks and bipartite graphs | Lab 09 | ||
| 11/13 | Explore project datasets | Lab 10 | Final project | |
| 11/20 | No class (project proposals due) | |||
| 11/27 | Projects | Groups meet with me individually |
Course requirements will include:
Attending class, doing assigned readings, and contributing to class discussions are all important to learning the material we will cover.
Part of almost every class will be devoted to working through hands-on labs. These labs will guide you through analysis of social networks problems and datasets. The labs will require you to build on the Python skills you will be learning in Data 8, The labs will also give you a chance to communicate the results of your analysis in writing.
The homework is an essential part of the learning that you will do in the class; there will be approximately 5 homework assignments, which will build on the concepts we discuss in class and the skills that we learn in the labs.
The final project will give you and a partner the opportunity to use a social networks dataset to pose a question, answer the question, and communicate your results in writing and through plots.