Belangrijke producten

We hebben nu gezien dat er een duidelijk patroon in onze graaf zit. Laten we een stap verder gaan dan alleen de structuur begrijpen. Gegeven de context van de graafstructuur, wat kunnen we hiervan leren? Bijvoorbeeld: wat drijft aankopen? Een goed startpunt is om te zoeken naar "belangrijke producten", bijvoorbeeld producten die iemand koopt en waarna die persoon nog iets anders koopt. We kunnen hier iets over afleiden met behulp van in-degree en out-degree. Eerst kijken we naar onze graaf en bekijken we de verdeling van de in-degree en out-degree, en gebruiken dat vervolgens om een werkdefinitie op te stellen van wat een "belangrijk product" is (iets met > X out-degrees en < Z in-degrees). Daarna maken we een subgraaf om te visualiseren hoe deze subgrafen eruitzien.

Deze oefening maakt deel uit van de cursus

Casestudies: netwerkanalyse in R

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Calculate the "out" degrees
out_degree <- ___(___, mode = "___")

## ... and "in" degrees
in_degree <- ___

# See the distribution of out_degree
___(___)

## ... and of in_degree
___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Casestudies: netwerkanalyse in R

SkillTag.level.beginnerSkillTag.label

4.8+

Begin de cursus gratis

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: Je gegevensset verkennen Exercise 2: Dyaden en triaden vinden Exercise 3: Clustering en wederkerigheid Exercise 4: Belangrijke producten

Huidige oefening

Exercise 5: Wat maakt een product belangrijk?Exercise 6: Tijdelijke structuur verkennen Exercise 7: Metrieken door de tijd Exercise 8: Statistieken in de tijd plotten

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.

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 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

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.

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