In this chapter, you will be introduced to fundamental concepts in social network analysis. You will learn how to use the <code>igraph</code> R package to explore and analyze social network data as well as learning how to visualize networks. 


What are social networks?

Creating an igraph object

Counting vertices and edges

Network attributes

Node attributes and subsetting

Edge attributes and subsetting

Visualizing attributes

Quiz on attributes

Network visualization

igraph network layouts

Visualizing edges

Quiz on igraph objects

Introduction to networks

In this chapter you will learn about directed networks.  You will also learn how to identify key relationships between vertices in a network as well as how to use these relationships to identify important or influential vertices. 
Throughout this chapter you will use a network of measles transmission. The data come from the German city of Hagelloch in 1861. Each directed edge of the network indicates a child becoming infected with measles after coming into contact with an infected child.



Directed networks

Directed igraph objects

Identifying edges for each vertex

Relationships between vertices

Neighbors

Distances between vertices

Finding longest path between two vertices

Important and influential vertices

Identifying key vertices

Betweenness

Visualizing important nodes and edges

Important vertices quiz

Identifying important vertices in a network

This module will show how to characterize global network structures and sub-structures. It will also introduce generating random network graphs.

Introduction

Forrest Gump network

Network density and average path length

Graph density quiz

Understanding network structures

Random graphs

Network randomizations

Randomization quiz

Network substructures

Triangles and transitivity

Transitivity randomizations

Cliques

Visualize largest cliques

Characterizing network structures

This chapter will further explore the partitioning of networks into sub-networks and determining which vertices are more highly related to one another than others.  You will also develop visualization methods by creating three-dimensional visualizations.


Close relationships: assortativity & reciprocity

Assortativity

Using randomizations to assess assortativity

Reciprocity

Assortativity quiz

Community detection

Fast-greedy community detection

Edge-betweenness community detection

Community quiz

Interactive network visualizations

Interactive networks with threejs

Sizing vertices in threejs

3D community network graph

Identifying special relationships

Friendship network data

Friendship network edge data

Friendship network node data

Forrest Gump network data

Measles network data

<b>Get an Introduction to Networks</b>
<br><br>
Discover the fundamental concepts in network analysis. This course begins by taking you through the basics of social networks, vertices and edges, and how you can use the igraph R package to explore and visualize network data.
<br><br>
You’ll move on to looking at directed networks in more detail, including the identification of key relationships between vertices and applying your new skills to a network data set looking at measles transmission in Hagelloch.
<br><br>
<b>Understand Network Structures and Graphs</b>
<br><br>
Learn to characterize network structures and substructures by looking at network density and average path length. The third chapter of this course takes you through randomization and random graphs, before moving on to triangles, transitivity, and visualizing cliques.
<br><br>
<b>Identify Relationships Using Assortativity in igraph</b>
<br><br>
Assortativity determines how likely two vertices are to be attached to each other if they share a common attribute - whether that’s numerical or categorical. You’ll explore the ASSORTATIVITY function within igraph to determine the impact of gender on a friendship network dataset, and will apply  randomizations to assess your findings.
<br><br>
<b>Create Interactive Network Plots using threejs</b>
<br><br>
At the end of this course, you’ll expand your knowledge beyond igraph to explore the network visualization capabilities of threejs. You’ll make your first interactive network plots using this R package, and will look at how you can further develop your visualization.

<h2>Get an Introduction to Networks</h2>
Discover the fundamental concepts in network analysis. This course begins by taking you through the basics of social networks, vertices and edges, and how you can use the igraph R package to explore and visualize network data.
<br><br>
You’ll move on to looking at directed networks in more detail, including the identification of key relationships between vertices and applying your new skills to a network data set looking at measles transmission in Hagelloch.

<h2>Understand Network Structures and Graphs</h2>
Learn to characterize network structures and substructures by looking at network density and average path length. The third chapter of this course takes you through randomization and random graphs, before moving on to triangles, transitivity, and visualizing cliques.

<h2>Identify Relationships Using Assortativity in igraph</h2>
Assortativity determines how likely two vertices are to be attached to each other if they share a common attribute - whether that’s numerical or categorical. You’ll explore the ASSORTATIVITY function within igraph to determine the impact of gender on a friendship network dataset, and will apply randomizations to assess your findings.

<h2>Create Interactive Network Plots using threejs</h2>
At the end of this course, you’ll expand your knowledge beyond igraph to explore the network visualization capabilities of threejs. You’ll make your first interactive network plots using this R package, and will look at how you can further develop your visualization.

Intermediate R

Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.

Network Analysis in R Course | Including igraph & threejs | DataCamp

Network Analysis in R

Randomization quiz

Network Analysis in R

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