Johns Hopkins University

Social Network Analysis

This course is part of multiple programs.

Ian McCulloh

Instructor: Ian McCulloh

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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
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.

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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

Ian McCulloh
Johns Hopkins University
7 Courses67 learners

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