Networked Life

NETS-1120

Fall 2022

Networked Life

NETS-1120

Fall 2022

Course Overview

Course Overview

How do infectious diseases spread? Why do some memes spread virally while others do not? Why do some teams or organizations perform better than others? Are we all really connected by six degrees of separation and, if so, how is that are our neighborhoods, workplaces, and social circles are so segregated? The answers to these questions and many more are all part of Network Science, a fascinating subject at the intersection of many disciplines, including computer science, communications, psychology, sociology, mathematics, and economics. This course will provide an introduction to the technical language of network science as well as to a collection of applications such as mathematical epidemiology, social contagion, games of cooperation and coordination, and collective problem solving.

Course Schedule

Course Schedule

Lecture 1 — Course Intro and Overview

1. SD, Chapter 1
2. HN, Chapter 1
3. Travers and Milgram (1969)
4. Optional: Watts and Strogatz (1998)
5. Optional: NPR Six Degrees Podcast https://www.wnycstudios.org/podcasts/undiscovered/episodes/six-degrees

Lecture 2 — How to read a scientific paper

1. Optional: https://www.sciencemag.org/careers/2016/03/how-seriously-read-scientific-paper#

Lecture 3 — Basic Terminology Part 1

1. SD, Chapter 2
2. HN, Chapters 2 and 5

Lecture 4 — Basic Terminology Part 2

Lecture 5 — Static Networks, Part 1

1. Leskovec and Horvitz (2007)
2. Ugander et al (2011)

Lecture 6 — Static Networks, Part 2

1. Optional: Feld (1991)
2. Optional: Newman et al (2002)

Lecture 7 — Inferring Networks from Data

1. De Choudury et al (2010)

Lecture 8 — Dynamic Networks

1. Yang et al (2021)
2. Optional: Kossinets and Watts (2006)
3. Optional: Kossinets and Watts (2009)

Lecture 9 — Random Networks

1. SD, Chapters 3-5
2. Amaral et al (2000)
3. Kleinberg (1999)
4. Optional: Watts and Strogatz (1998)
5. Optional: Kleinberg (2000)
6. Optional: Watts et al (2002)

Lecture 10 — Clustering and Navigation, Part 1

Lecture 11 — Clustering and Navigation, Part 2

Review for Midterm
Midterm Exam (25% of grade)
Fall Break

Lecture 12 — Replicating the Small-World Experiment

1. Goel et al (2009)
2. Optional: Dodds et al (2003)

Lecture 13 — Intro to Epidemics

1. SD, Chapter 6
2. HN Chapter 3
3. Chang et al 2020
4. Optional: Clauset Network Epi
5. Optional: Watts et al (2005)
6. Optional: Keeling and Eames (2005)
7. Optional: Pastor Satorras and Vespigani (2001)

Lecture 14 — Unpredictability and Resurgence

Lecture 15 — Social Influence and Collective Decisions

1. SD, Chapters 7-8
2. HN Chapters 7-8
3. Granovetter (1978)
4. Optional: Watts (2002)

Lecture 16 — Social Influence and Collective Decisions, Part 2

Lecture 17 — Inequality and Unpredictability in Cultural Markets, Part 1

1. Macy et al (2019)
2. Epstein et al (2021)
3. Optional: Salganik et al (2006)

Lecture 18 — Inequality and Unpredictability in Cultural Markets, Part 2

Lecture 19 — Structure of Online Virality

1. Bakshy et al (2011)
2. Cheng et al (2014)
3. Martin et al (2016)
4. Optional: Goel et al (2012)
5. Optional: Goel et al (2015)
6. Optional: https://www.npr.org/2017/10/03/539523369/live-episode-buzzfeed-jonah-peretti 

Lecture 20 — Predicting Virality, Part 1

Lecture 21 — Predicting Virality, Part 2
Thanksgiving

Lecture 22 — Cooperation on Static Networks

1. Axelrod and Hamilton (1981)
2. Embrey et al (2018)
3. Optional: Suri and Watts (2011)
4. Optional: Wang, Suri, and Watts (2012)

Lecture 23 — Cooperation on Dynamic Networks

Lecture 24 — Collaboration on Networks

1. Jacobs and Watts (2021)
2. Optional: Kearns and Suri (2006)
3. Optional: Mason and Watts (2012)
4. Optional: Shore et al (2015)
5. Optional: Mao et al (2016)
6. Optional: Almaatouq et al (2020)

Lecture 25 — Informal Networks in Firms

Review for Final
Final Exam (35% of grade)