Class timing

Holi Semester 2022
Tuesday and Friday: 2 - 3:30 PM (Slot AC)

Textbooks

• Social Network Analysis by Tanmoy Chakraborty.
• Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg.

Tentative course content

1. Social network analysis
   • Introduction, applications and challenges
   • Structural properties of large social networks
   • Measures of network centrality
   • Identifying popular users/experts
   • Community detection algorithms
   • Information diffusion models
   • Influence maximization
   • Link prediction
2. Fundamentals of social data analytics
   • Measurement and collection of social media data
   • Basics of text processing over social data
   • Entity linking and entity resolution
   • Topic models
3. Social search and recommendation algorithms
4. Crowdsourcing
5. Harmful users/content on social media -- hate speech, fake news, spammers in social networks, etc.
6. Different types of social platforms -- Anonymous social networks, E-commerce sites
7. Basics of computational social choice — voting, allocation
8. Social media and digital health

Pre-requisites for the course

• Data structures and algorithms
• Basics of Machine Learning
• The course will involve understanding of research papers, and some programming experience.

Course evaluation

• Minor and Major Exams: 30% + 35%
• Course Project: 35%
• Class Participation: Bonus 5%

Note: The evaluation plan is subject to change based on institute/department policies and other related factors.