Fall 2016

L&S 88-4: Social Networks

Class meetings: Mondays, 2-4pm (458 Evans Hall)

Instructor: Dennis M. Feehan (feehan@berkeley.edu)

Office hours: Wednesdays, 3-4pm (2232 Piedmont Ave, Rm 210), or by appointment

Class number: 33573

Piazza page


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.


Course requirements will include:


NB: This syllabus is approximate, and will be adjusted as the semester progresses.

Date Module Reading Assignment
8/29 Introduction and course overview
9/5 LABOR DAY (no class)
9/12 Personal networks Lab 01 Hwk 02
9/19 Homophily and personal networks Lab 02
9/26 Intro to working with complete network data Lab 03 Hwk 03 (due Oct. 11)
10/3 Quantifying network structure Lab 04
10/10 The Erdos-Renyi random network model Lab 05
10/17 Clustering in social networks Lab 06
10/24 Attributes and assortativity Lab 07
10/31 Dynamics and centrality Lab 08
11/7 Affiliation networks and bipartite graphs Lab 09 Hwk 04 (due Nov. 15)