ABOUT THE COURSE:Networks are a fundamental tool for modeling complex social, technological, and biological systems. Coupled with the emergence of online social networks and large-scale data availability in social sciences, this course focuses on the analysis of massive networks which provide many computational, algorithmic, and modeling challenges. The course will cover research on the structure and analysis of such large networks and on models and algorithms that abstract their basic properties. We will explore how to practically analyze large-scale network data and how to reason about it through models for network structure and evolution. Topics covered in this course are how information spreads through society; robustness and fragility of networks; algorithms for the World Wide Web; prediction and recommendation in online social networks; representation learning for large networks; etc.PREREQUISITES: Python programming, Probability and Statistics, Machine LearningINDUSTRY SUPPORT: Any social media company, E-commerce company, etc
Social Network Analysis
Indraprastha Institute of Information Technology Delhi and NPTEL via Swayam
This course may be unavailable.
Overview
Syllabus
Week 1: Introduction (History, Important tools and datasets)
Week 2:Network Measures
Week 3:Network Growth Models
Week 4:Link Analysis
Week 5:Community Detection - part 2
Week 6:Community Detection - part 2
Week 7:Link prediction
Week 8:Cascade Behavior
Week 9:Anomaly detection
Week 10:Graph Representation Learning - part 1
Week 11:Applications
Week 12:Lessons learned, Conclusion
Taught by
Prof. Tanmoy Chakraborty
