Offered: Fall 2015, Fall 2018, Spring 2013, Spring 2015, Spring 2019, Spring 2020, Spring 2021, Fall 2021, Spring 2022, Fall 2022

Aims and Objectives

In today’s world almost everything is connected. With the help of social networking websites people are even more connected than ever. The objective of this course is to learn methods to analyze social networks. Methods that students will learn include: identifying important nodes in a network, detecting communities, modeling social networks, analyzing information diffusion and opinion formation. Some of the methods can also be applied to networks not involving human relations like technological or semantic networks. At the end of the course, students will be able to apply the concepts covered in this course on real life and online social networks.

Weekly Contents

1. Introduction, Network Mathematics
2. How to use gephi and networkx
3. Strong and Weak Ties
4. Centrality (undirected)
5. Centrality (directed)
6. Web Structure, Web Search
7. PageRank, Spectral Analysis
8. Applications of centrality
9. Community detection algorithms, Traditional methods
10. Betweenness, Modularity, Testing communities
11. Random Graph Model, Alternate Models
12. Preferential Attachment, Small world networks
13. Small world models, Motifs
14. Information diffusion, Network resilience
15. Research topics in SNA

Textbook(s)

1. David Easley and Jon Kleinberg (2010). Networks, Crowds and Markets. Cambridge University Press
2. Albert-László Barabási (2015). Network Science. Published Online
3. Mark Newman (2018). Networks: An Introduction (Second Edition) OUP, Oxford