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Social Network Analysis

Ce cours n'est pas disponible en Français (France)

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Social Network Analysis

Ce cours fait partie de plusieurs programmes.

Instructeur : Ian McCulloh

2 770 déjà inscrits

Inclus avec

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

11 avis

niveau Intermédiaire

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

11 avis

niveau Intermédiaire

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Dependency Analysis
Catégorie : Sociology
Catégorie : Social Network Analysis
Catégorie : Statistical Methods
Catégorie : Social Sciences
Catégorie : Statistical Hypothesis Testing
Catégorie : Network Model
Catégorie : Graph Theory
Catégorie : Statistical Analysis
Catégorie : Network Analysis
Catégorie : Statistical Modeling

Outils que vous découvrirez

Catégorie : R Programming
Catégorie : Statistical Software
Catégorie : R (Software)

Détails à connaître

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9 devoirs

Enseigné en Anglais

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Il y a 4 modules dans ce cours

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.

Détails du module

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.

Inclus

1 lecture1 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.

Inclus

6 vidéos1 lecture3 devoirs1 laboratoire non noté

6 vidéosTotal 77 minutes

Terminology24 minutes
Degree Centrality17 minutes
Betweenness Centrality14 minutes
Closeness Centrality7 minutes
Centrality PE8 minutes
Graph Level Measures7 minutes

1 lectureTotal 10 minutes

Reading References10 minutes

3 devoirsTotal 90 minutes

Graph Theory and Centrality Measures60 minutes
Introduction to Graph Theory and Network Types18 minutes
Centrality Measures in Social Networks12 minutes

1 laboratoire non notéTotal 60 minutes

Practice Lab: Graph Theory & Centrality Measures60 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.

Inclus

4 vidéos2 lectures3 devoirs1 laboratoire non noté

4 vidéosTotal 79 minutes

Social Theory Part 1 27 minutes
Social Theory Part 230 minutes
Social Theory Part 310 minutes
Organizational Theories12 minutes

2 lecturesTotal 55 minutes

Reading References15 minutes
Self-Reflective Reading: Looking Glass Self40 minutes

3 devoirsTotal 90 minutes

Centralization and Social Theory60 minutes
Understanding Social Forces and Link Formation12 minutes
Social Theories and Organizational Networks18 minutes

1 laboratoire non notéTotal 60 minutes

Practice Lab: Social Network Analysis Using R60 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’.

Inclus

3 vidéos1 lecture3 devoirs1 laboratoire non noté

3 vidéosTotal 58 minutes

Exponential Random Graph Models (ERGM)22 minutes
ERGM Example - Gray's Anatomy17 minutes
Stochastic Actor Oriented Models (SAOM)19 minutes

1 lectureTotal 10 minutes

Reading References10 minutes

3 devoirsTotal 90 minutes

Network Statistical Models60 minutes
Structural Dependence and Statistical Analysis in Networks12 minutes
Advanced Network Modeling with Exponential Random Graphs and SAOM18 minutes

1 laboratoire non notéTotal 60 minutes

Practice Lab: Network Analysis Using ERGM & RSiena Models with the s50 Dataset60 minutes

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