Social Network Analysis: Statistical Models - JHU
Master advanced network analysis techniques using R for analyzing social structures and relationships.
Master advanced network analysis techniques using R for analyzing social structures and relationships.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Social Computing Specialization or Social Media Analytics Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Calculate and interpret key centrality measures
Apply statistical models to analyze network relationships
Understand foundational social theories
Construct and analyze various network types
Implement ERGM and SAOM models
Conduct hypothesis testing with empirical data
Skills you'll gain
This course includes:
3.5 Hours PreRecorded video
9 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores advanced social network analysis, combining theoretical foundations with practical applications. Students learn to analyze complex social structures using statistical methods and R programming, focusing on centrality measures, graph theory, and social forces. The curriculum covers exponential random graph models (ERGM) and stochastic actor-oriented models (SAOM) using tools like 'statnet' and 'RSiena', preparing students for advanced network analytics.
Course Introduction
Module 1 · 14 Minutes to complete
Graph Theory and Centrality Measures
Module 2 · 4 Hours to complete
Centralization and Social Theory
Module 3 · 4 Hours to complete
Network Statistical Models
Module 4 · 3 Hours to complete
Fee Structure
Instructor
Pioneering Social Network Analysis and AI at Johns Hopkins University
Dr. Ian McCulloh is an esteemed associate professor at Johns Hopkins University, holding joint appointments in the Bloomberg School of Public Health and the Whiting School of Engineering. His research focuses on social neuroscience, social network analysis, and the application of artificial intelligence to enhance understanding of online influence and strategic communication. With over 100 peer-reviewed publications and several influential books, including Social Network Analysis with Applications and ISIS in Iraq: Understanding the Social and Psychological Foundations of Terror, Dr. McCulloh has established himself as a leading voice in his field. He also founded the Brain Rise Foundation, a nonprofit dedicated to advancing neuroscience research for substance abuse recovery. Prior to his academic career, he had a distinguished military service, retiring as a Lieutenant Colonel after 20 years, during which he led innovative projects in data-driven social science research for countering extremism. Dr. McCulloh's multifaceted expertise and commitment to applying science for societal benefit make him a valuable asset to both academia and public health initiatives.
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