Compare Graphs of Different User Types

One of the easiest ways we can see differences between the graphs is by plotting a small subgraph of each one. If different patterns are strong, they should be easy to visually detect. As you look at these two different plots, what stands out? It's worth taking a moment to think about the differences between these two populations. While there's doubtless overlap between these two groups, what are some different traits between each population? Subscribers are probably regular users, local, and might be more likely to travel to more remote parts of the graph. Customers are likely tourists or locals who don't bike regularly. Their use is probably more centered in major stations, and they might ride more for leisure in some of the touristy parts of the city.

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.

# Induce a subgraph from subscriber_trip_graph of the first 12 vertices
twelve_subscriber_trip_graph <- induced_subgraph(___,___)

# Plot the subgraph and title it "Subscribers"
plot(
  ___, 
  main = "___"
)

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 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

In this chapter you will analyze data from a Chicago bike sharing network. We will build on the concepts already covered in the introductory course, and add a few new ones to handle graphs with weighted edges. You will also start with data in a slightly more raw form and cover how to build your graph up from a data source you might find.

Exercise 1: Creating our graph from raw data Exercise 2: Making Graphs of Different User Types Exercise 3: Compare Graphs of Different User Types

Current Exercise

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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Exercise 1: Other packages for plotting graphs!Exercise 2: ggnet Basics Exercise 3: ggnetwork Basics Exercise 4: More ggnet Plotting Options Exercise 5: More ggnetwork Plotting Options Exercise 6: Interactive visualizations Exercise 7: Interactive plots with ggiraph Exercise 8: Interactive javascript plots Exercise 9: Alternative visualizations Exercise 10: Alternative ways to visualize a graph: Hive plots Exercise 11: BioFabric as an HTML widget Exercise 12: Plotting graphs on a map