Class timing

Diwali Semester 2023
Monday and Thursday: 3:30 - 4:50 PM (Slot AB)

Reading materials

• Networks: An Introduction (2nd Edition) by Mark Newman.
• Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg.
• Lecture slides and relevant research papers.

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.
• Working knowledge of AI/ML would be a plus.
• The course will involve understanding of research papers.
• The project component requires some programming experience.

Course evaluation

• Minor and major exams: 30% + 35%
• Course project: 30%
• Bonus 5% if the final project outcome is worthy of a top-tier publication.
• Attendance and class participation: 5%