Social Network Analysis

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

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