Visualize nodes by degree

Now that we've taken a look at our graph and explored some of the basic properties of it, let's think a bit more about our network. We observed that there are some highly connected nodes and many outlier points. We visualize this by conditionally plotting the graph and coloring some of the nodes by in and out degree. Let's think about the nodes as three different types:

high retweeters and highly retweeted.
users who retweeted only once (have an in-degree of 0 and an out-degree of 1).
users who were retweeted only once (have an in-degree of 1 and an out-degree of 0).

This will help us get more information about what's going on in the ring around the cluster of highly connected nodes.

This exercise is part of the course

Case Studies: Network Analysis in R

Hands-on interactive exercise

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

# Calculate the "in" degree distribution of retweet_graph
in_deg <- degree(___, mode = "___")

# Calculate the "out" degree distribution of retweet_graph
out_deg <- degree(___, mode = "___")

# Find the case with one "in" degree and zero "out" degrees
has_tweeted_once_never_retweeted <- __ == 1 & __ == 0

# Find the case with zero "in" degrees and one "out" degree
has_never_tweeted_retweeted_once <- __ == 0 & __ == 1

Edit and Run Code

This exercise is part of the course

Case Studies: Network Analysis in R

BeginnerSkill Level

4.7+

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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.

Exercise 1: Exploring your data set Exercise 2: Finding Dyads and Triads Exercise 3: Clustering and Reciprocity Exercise 4: Important Products Exercise 5: What Makes an Important Product?Exercise 6: Exploring temporal structure Exercise 7: Metrics through time Exercise 8: Plotting Metrics Over Time

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Exercise 1: Creating your retweet graph Exercise 2: Visualize the graph Exercise 3: Visualize nodes by degree

Current Exercise

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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Exercise 1: Creating our graph from raw data Exercise 2: Making Graphs of Different User Types Exercise 3: Compare Graphs of Different User Types Exercise 4: Compare graph distance vs. geographic distance Exercise 5: Compare Subscriber vs. Non-Subscriber Distances Exercise 6: Most Traveled To and From Stations Exercise 7: Most Traveled To and From Stations with Weights Exercise 8: Visualize central vertices Exercise 9: Weighted Measures of Centrality Exercise 10: Connectivity Exercise 11: Find the minimum cut 1 Exercise 12: Find the minimum cut 2 Exercise 13: Unweighted Clustering Randomizations Exercise 14: Weighted Clustering Randomizations

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