Visualize the graph

Now that we've thought a bit about how we constructed our network, let's size our graph and make an initial visualization. Before you make the plot, you'll calculate the number of vertices and edges. This allows you to know if you can actually plot the entire network. Also, the ratio of nodes to edges will give you an intuition of just how dense or sparse your plot might be. As you create your plot, take a moment to hypothesize what you think the plot will look like based on these metrics.

This exercise is part of the course

Case Studies: Network Analysis in R

Exercise instructions

Calculate the number of nodes of the graph retweet_graph using gorder().
Calculate the number of edges of the graph retweet_graph using gsize().
Calculate graph density of the graph retweet_graph using graph.density().
Plot the retweet network retweet_graph.

Hands-on interactive exercise

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

# Count the number of nodes in retweet_graph
___

# Count the number of edges in retweet_graph
___

# Calculate the graph density of retweet_graph
___

# Plot retweet_graph
plot(___)

Edit and Run Code

This exercise is part of the course

Case Studies: Network Analysis in R

BeginnerSkill Level

4.8+

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In this chapter you'll explore a subset of an Amazon purchase graph. You'll build on what you've already learned, finding important products and discovering what drives purchases. You'll also examine how graphs can change through time by looking at the graph during different time periods.

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Current Exercise

Exercise 3: Visualize nodes by degree Exercise 4: What's the distribution of centrality?Exercise 5: Who is important in the conversation?Exercise 6: Plotting important vertices Exercise 7: Building a mentions graph Exercise 8: Comparing mention and retweet graph Exercise 9: Assortativity and reciprocity Exercise 10: Finding who is talking to whom Exercise 11: Finding communities Exercise 12: Comparing community algorithms Exercise 13: Visualizing the communities

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