1.022 | Fall 2018 | Undergraduate

Introduction to Network Models


[E] = Easley, David, and Jon Kleinberg. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, 2010. ISBN: 9780521195331. [Preview with Google Books]

Note: An online version of the above textbook is freely available on Jon Kleinberg’s homepage on Cornell University’s Department of Computer Science website.

[J] = Jackson, Matthew O. Social and Economic Networks. Princeton University Press, 2010. ISBN: 9780691148205.

[N] = Newman, M.E.J. Networks: An Introduction. Oxford University Press, 2010. ISBN: 9780199206650. [Preview with Google Books]

[S] = Shah, Devavrat. Gossip Algorithms. Now Publishers Inc., 2009. ISBN: 9781601982360.

1 Course speci fics, motivation, and intro to graph theory

[E] Chapter 1. [Preview with Google Books]

[E] Chapter 2.1. [Preview with Google Books]

2 Introduction to graph theory

[E] Chapter 2.2. [Preview with Google Books]

[E] Chapter 2.3. [Preview with Google Books]

[N] Chapter 6.1–6.4, 6.7, and 6.9.

3 Strong and weak ties, triadic closure, and homophily

[E] Chapter 3.1. [Preview with Google Books]

[E] Chapter 3.2. [Preview with Google Books]

[E] Chapter 3.3. [Preview with Google Books]

[E] Chapter 4.1. [Preview with Google Books]

4 Centrality measures

[N] Chapter 7.1–7.3 and 7.6–7.7.

5 Centrality and web search, spectral graph theory

[E] Chapter 14.1. [Preview with Google Books]

[E] Chapter 14.2. [Preview with Google Books]

[E] Chapter 14.3. 

[N] Chapter 6.13.

6–7 Spectral graph theory, spectral clustering, and community detection

[N] Chapter 11.5. 

Spielman, Daniel. Chapter 16: Spectral Graph Theory (PDF). Yale University, 2007.

8–10 Network models

[N] Chapter 12.1–12.3, 12.5, and 12.7–12.8.

11 Con figuration model and small-world graphs

[E] Chapter 20.1–20.2.

[J] Chapter 4.1.4.

[J] Chapter 4.2.1. [Preview with Google Books]

[J] Chapter 4.2.6. 

[N] Chapter 15.1.

12 Growing networks

[E] Chapter 18.  

[N] Chapter 14.2.

Midterm exam
13–14 Linear dynamical systems

[N] Chapter 18.1–18.2.

15 Markov chains [N] Chapter 6.14.
16–17 Information spread and distributed computation

[S] Chapter 2.1. [Preview with Google Books]

[S] Chapter 2.2. [Preview with Google Books]

[S] Chapter 3.1. [Preview with Google Books]

[S] Chapter 3.2. [Preview with Google Books]

[S] Chapter 5.1–5.2. 

18–19 Learning and herding

[E] Chapter 16.1–16.2.

[J] Chapter 8.3 and 9.1.

20 Epidemics

[E] Chapter 21.

[J] Chapter 7.1–7.2

21 Introduction to game theory I

[E] Chapter 6.1–6.4 and 6.10 (A & B).

22 Introduction to game theory II

[E] Chapter 6.5, 6.6, and 6.9.

23 Application of game theory to networks

[E] Chapter 8.1–8.3. 

24 Course review and discussion No readings assigned.
25 Project presentations No readings assigned.

Course Info

As Taught In
Fall 2018
Learning Resource Types
Problem Sets
Lecture Notes