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Social Network Analysis
  • Level Foundation
  • Duration 10 hours
  • Course by Johns Hopkins University
  • Offered by

About

This course is designed to quite literally ‘make a science’ out of something at the heart of society: social networks. Humans are natural network scientists, as we compute new network configurations all the time, almost unaware, when thinking about friends and family (which are particular forms of social networks), about colleagues and organizational relations (other, overlapping network structures), and about how to navigate delicate or opportunistic network configurations to save guard or advance in our social standing (with society being one big social network itself). While such network structures always existed, computational social science has helped to reveal and to study them more systematically. In the first part of the course we focus on network structure. This looks as static snapshots of networks, which can be intricate and reveal important aspects of social systems. In our hands-on lab, you will also visualize and analyze a network with a software yourself, which will help to appreciate the complexity social networks can take on. During the second part of the course, we will look at how networks evolve in time. We ask how we can predict what kind of network will form and if and how we could influence network dynamics.

Modules

Untitled Lesson

    • 1 Readings
    • 1 Plugin
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1 Readings

  • Course Overview

Introduction to Graph Theory and Network Types

    • 3 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Introduction to Graph Theory and Network Types

3 Videos

  • Terminology
  • Degree Centrality
  • Betweenness Centrality

1 Readings

  • Reading References

Centrality Measures in Social Networks

    • 3 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Centrality Measures in Social Networks

3 Videos

  • Closeness Centrality
  • Centrality PE
  • Graph Level Measures

1 Readings

  • Reading References

Module-end Assessments

    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Graph Theory and Centrality Measures

1 Labs

  • Practice Lab: Graph Theory & Centrality Measures

Understanding Social Forces and Link Formation

    • 2 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Understanding Social Forces and Link Formation

2 Videos

  • Social Theory Part 1
  • Social Theory Part 2

1 Readings

  • Reading References

Social Theories and Organizational Networks

    • 2 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Social Theories and Organizational Networks

2 Videos

  • Social Theory Part 3
  • Organizational Theories

1 Readings

  • Reading References

Module-end Assessments

    • 1 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Centralization and Social Theory

1 Labs

  • Practice Lab: Social Network Analysis Using R

1 Readings

  • Self-Reflective Reading: Looking Glass Self

Structural Dependence and Statistical Analysis in Networks

    • 2 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Structural Dependence and Statistical Analysis in Networks

2 Videos

  • Exponential Random Graph Models (ERGM)
  • ERGM Example - Gray's Anatomy

1 Readings

  • Reading References

Advanced Network Modeling with Exponential Random Graphs and SAOM

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Advanced Network Modeling with Exponential Random Graphs and SAOM

1 Videos

  • Stochastic Actor Oriented Models (SAOM)

1 Readings

  • Reading References

Module-end Assessments

    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Network Statistical Models

1 Labs

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

Auto Summary

Discover the science of social networks in this engaging course on Big Data and Analytics. Taught by expert instructor Coursera, this foundational course delves into network structures and their evolution over time. Through hands-on labs, visualize and analyze complex social networks. Ideal for beginners, the course spans 600 minutes and is available through a Starter subscription. Perfect for those interested in computational social science and network dynamics.

Ian McCulloh
Instructor

Ian McCulloh