The "Social Network Analysis" course offers a comprehensive exploration of the intricate relationships within social networks, emphasizing the theoretical and practical applications of network analysis. Through engaging modules, learners will delve into advanced topics in graph theory, centrality measures, and statistical modeling, equipping them with the skills to analyze and interpret social structures effectively.
Social Network Analysis
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Social Network Analysis
This course is part of multiple programs.
Instructor: Ian McCulloh
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
13 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Learn to calculate and interpret key centrality measures to identify influential nodes in social networks.
Gain skills in applying statistical models to analyze relationships and dynamics within social networks.
Understand how foundational social theories inform network analysis and shape interpretations of social interactions.
Skills you'll gain
Details to know
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Recently updated!
September 2024
Assessments
9 assignments
Taught in English
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There are 4 modules in this course
This course explores the intersection of social theories and statistical analysis within social networks, focusing on structural dependence and its implications. Students will engage in hypothesis testing of social forces using empirical data, and will learn to construct networks and model longitudinal behavior with tools such as 'statnet' and 'RSiena.' Key terminology and the hierarchy of social link formation will be emphasized, alongside practical calculations of fundamental graph and network measures like Density and Degree. Additionally, students will differentiate between various network types and centrality measures, equipping them with a comprehensive understanding of social network analysis.
What's included
1 reading1 plugin
In this module, you will explore advanced topics in graph theory and centrality measures as applied to social networks. You will learn to identify key influencers, measure network cohesion, and strategize interventions based on network structure and dynamics.
What's included
6 videos2 readings3 assignments1 ungraded lab
In this module, you will explore Graph Theory and Centrality Measures, delving into the dynamics of social networks. You will also learn to distinguish between the six social forces and understand the hierarchical formation of social links. Discuss foundational social theories that underpin social network analysis, providing insights into how these theories shape organizational networks and societal interactions. This module equips you with essential knowledge to analyze and interpret the intricate relationships within social structures.
What's included
4 videos3 readings3 assignments1 ungraded lab
In this module, you will explore Network Statistical Methods through a comprehensive study of structural dependence and its impact on statistical analysis. You will also learn to calculate link likelihoods manually and conduct hypothesis testing on social forces using empirical data. You will also gain practical skills in constructing Exponential Random Graph Models (ERGM) using ‘statnet’ in R and modeling longitudinal network behavior with Stochastic Actor Oriented Models (SAOM) using ‘RSiena’.
What's included
3 videos2 readings3 assignments1 ungraded lab
Instructor
Offered by
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University of California, Davis
University of Michigan
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Stanford University
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Frequently asked questions
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