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.

Exploring your data set

Finding Dyads and Triads

Clustering and Reciprocity

Important Products

What Makes an Important Product?

Exploring temporal structure

Metrics through time

Plotting Metrics Over Time

Exploring graphs through time

In this lesson you'll explore some Twitter data about R by looking at conversations using '#rstats'.  First you'll look at the raw data and think about how you want to build your graph.  There's a number of ways to do this, and we'll cover two ways: retweets and mentions.  You'll build those graphs and then compare them on a number of metrics.

Creating your retweet graph

Visualize the graph

Visualize nodes by degree

What's the distribution of centrality?

Who is important in the conversation?

Plotting important vertices

Building a mentions graph

Comparing mention and retweet graph

Assortativity and reciprocity

Finding who is talking to whom

Finding communities

Comparing community algorithms

Visualizing the communities

How do people talk about R on Twitter?

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.

Creating our graph from raw data

Making Graphs of Different User Types

Compare Graphs of Different User Types

Compare graph distance vs. geographic distance

Compare Subscriber vs. Non-Subscriber Distances

Most Traveled To and From Stations

Most Traveled To and From Stations with Weights

Visualize central vertices

Weighted Measures of Centrality

Connectivity

Find the minimum cut 1

Find the minimum cut 2

Unweighted Clustering Randomizations

Weighted Clustering Randomizations

Bike sharing in Chicago

So far everything we've done has been using plotting from igraph. It provides many powerful ways to plot your graph data.  However many people prefer interacting with other plotting frameworks like ggplot2, or even interactive frameworks like d3.js. In this lesson you'll look at other plotting libraries that build on the ggplot2 framework. You'll also look at other non-"hairball" type methods like hive plots, as well as building interactive and animated plots.

Other packages for plotting graphs!

ggnet Basics

ggnetwork Basics

More ggnet Plotting Options

More ggnetwork Plotting Options

Interactive visualizations

Interactive plots with ggiraph

Interactive javascript plots

Alternative visualizations

Alternative ways to visualize a graph: Hive plots

BioFabric as an HTML widget

Plotting graphs on a map

Other ways to visualize graph data

Amazon graph

Amazon purchase graph over time

Twitter retweet graph

Twitter mention graph

Bike sharing data

Now that you're familiar with the basics of network analysis it's time to see how to apply those concepts to large real-world data sets. You'll work through three different case studies, each building on your previous work. These case studies are working with the kinds of data you'll see in both academic and industry settings. We'll explore some of the computational and visualization challenges you'll face and how to overcome them. Your knowledge of igraph will continue to grow, but we'll also leverage other visualization libraries that will help you bring your visualizations to the web.

Network Analysis in R

Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.

Case Studies: Network Analysis in R

Instructor

A map of the graph

Graph distance

Graph distance vs geographic distance

Geographic distance

Let's practice!

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Bike sharing in Chicago - Compare graph distance vs. geographic distance

Compare graph distance vs. geographic distance

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