Fast-greedy community detection

The first community detection method you will try is fast-greedy community detection. You will use the Zachary Karate Club network. This social network contains 34 club members and 78 edges. Each edge indicates that those two club members interacted outside the karate club as well as at the club. Using this network you will determine how many sub-communities the network has and which club members belong to which subgroups. You will also plot the networks by community membership.

This exercise is part of the course

Network Analysis in R

Exercise instructions

Use the function fastgreedy.community() to create a community object. Assign this to the object kc.
Use the function sizes() on kc to determine how many communities were detected and how many club members are in each.
Display which club members are in which community using the function membership().
Make the default community plot by using the function plot(). The first argument should be the object kc and the second argument is the graph object g.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Perform fast-greedy community detection on network graph
kc = ___(g)

# Determine sizes of each community
___(___)

# Determine which individuals belong to which community
___(___)

# Plot the community structure of the network
___(___, g)

Edit and Run Code

This exercise is part of the course

Network Analysis in R

IntermediateSkill Level

4.8+

Start Course for Free

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.

Exercise 1: What are social networks?Exercise 2: Creating an igraph object Exercise 3: Counting vertices and edges Exercise 4: Network attributes Exercise 5: Node attributes and subsetting Exercise 6: Edge attributes and subsetting Exercise 7: Visualizing attributes Exercise 8: Quiz on attributes Exercise 9: Network visualization Exercise 10: igraph network layouts Exercise 11: Visualizing edges Exercise 12: Quiz on igraph objects

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.

Exercise 1: Directed networks Exercise 2: Directed igraph objects Exercise 3: Identifying edges for each vertex Exercise 4: Relationships between vertices Exercise 5: Neighbors Exercise 6: Distances between vertices Exercise 7: Finding longest path between two vertices Exercise 8: Important and influential vertices Exercise 9: Identifying key vertices Exercise 10: Betweenness Exercise 11: Visualizing important nodes and edges Exercise 12: Important vertices quiz

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

Exercise 1: Introduction Exercise 2: Forrest Gump network Exercise 3: Network density and average path length Exercise 4: Graph density quiz Exercise 5: Understanding network structures Exercise 6: Random graphs Exercise 7: Network randomizations Exercise 8: Randomization quiz Exercise 9: Network substructures Exercise 10: Triangles and transitivity Exercise 11: Transitivity randomizations Exercise 12: Cliques Exercise 13: Visualize largest cliques

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.

Exercise 1: Close relationships: assortativity & reciprocity Exercise 2: Assortativity Exercise 3: Using randomizations to assess assortativity Exercise 4: Reciprocity Exercise 5: Assortativity quiz Exercise 6: Community detection Exercise 7: Fast-greedy community detection

Current Exercise

Exercise 8: Edge-betweenness community detection Exercise 9: Community quiz Exercise 10: Interactive network visualizations Exercise 11: Interactive networks with threejs Exercise 12: Sizing vertices in threejs Exercise 13: 3D community network graph