Social Network Analysis

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Social Network Analysis

This course is part of multiple programs.

Instructor: Ian McCulloh

2,364 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

11 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

11 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 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

Tools you'll learn

R Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

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There are 4 modules in this course

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.

By completing this course, learners will gain a solid understanding of how to identify key influencers, measure network cohesion, and conduct hypothesis testing using empirical data. What sets this course apart is its blend of theoretical foundations and hands-on experience using R programming for network analysis, specifically with tools like 'statnet' and 'RSiena.' Whether you’re looking to enhance your skills in data analysis or seeking to understand the dynamics of social behavior, this course will serve as a vital resource. With a focus on real-world applications, learners will emerge equipped to tackle complex social phenomena, making significant contributions to their fields.

This course explores the intersection of social theories and statistical analysis within social networks, focusing on structural dependence and its implications. You 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, you will be able to 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 videos1 reading3 assignments1 ungraded lab

6 videos Total 77 minutes

Terminology 24 minutes
Degree Centrality 17 minutes
Betweenness Centrality 14 minutes
Closeness Centrality 7 minutes
Centrality PE 8 minutes
Graph Level Measures 7 minutes

1 reading Total 10 minutes

Reading References 10 minutes

3 assignments Total 90 minutes

Introduction to Graph Theory and Network Types 18 minutes
Centrality Measures in Social Networks 12 minutes
Graph Theory and Centrality Measures 60 minutes

1 ungraded lab Total 60 minutes

Practice Lab: Graph Theory & Centrality Measures 60 minutes

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. You will 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 videos2 readings3 assignments1 ungraded lab

4 videos Total 79 minutes

Social Theory Part 1 27 minutes
Social Theory Part 2 30 minutes
Social Theory Part 3 10 minutes
Organizational Theories 12 minutes

2 readings Total 55 minutes

Reading References 15 minutes
Self-Reflective Reading: Looking Glass Self 40 minutes

3 assignments Total 90 minutes

Understanding Social Forces and Link Formation 12 minutes
Social Theories and Organizational Networks 18 minutes
Centralization and Social Theory 60 minutes

1 ungraded lab Total 60 minutes

Practice Lab: Social Network Analysis Using R 60 minutes

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 videos1 reading3 assignments1 ungraded lab

3 videos Total 58 minutes

Exponential Random Graph Models (ERGM) 22 minutes
ERGM Example - Gray's Anatomy 17 minutes
Stochastic Actor Oriented Models (SAOM) 19 minutes

1 reading Total 10 minutes

Reading References 10 minutes

3 assignments Total 90 minutes

Structural Dependence and Statistical Analysis in Networks 12 minutes
Advanced Network Modeling with Exponential Random Graphs and SAOM 18 minutes
Network Statistical Models 60 minutes

1 ungraded lab Total 60 minutes

Practice Lab: Network Analysis Using ERGM & RSiena Models with the s50 Dataset 60 minutes

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Instructor

Ian McCulloh

Johns Hopkins University

17 Courses 24,178 learners

Offered by

Johns Hopkins University

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