Visualizza il grafo

Ora che abbiamo riflettuto su come abbiamo costruito la nostra rete, dimensioniamo il grafo e creiamo una prima visualizzazione. Prima di tracciare il grafico, calcolerai il numero di vertici e di archi. Questo ti permette di capire se puoi effettivamente rappresentare l’intera rete. Inoltre, il rapporto tra nodi e archi ti darà un’idea di quanto denso o rado potrebbe essere il grafico. Mentre crei il grafico, prova a formulare un’ipotesi su come pensi che apparirà sulla base di queste metriche.

Questo esercizio fa parte del corso

Casi di studio: analisi di reti in R

Visualizza il corso

Istruzioni dell'esercizio

Calcola il numero di nodi del grafo retweet_graph usando gorder().
Calcola il numero di archi del grafo retweet_graph usando gsize().
Calcola la densità del grafo retweet_graph usando graph.density().
Disegna la rete di retweet retweet_graph.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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(___)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Casi di studio: analisi di reti in R

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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

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: Crea il tuo grafo dei retweet Exercise 2: Visualizza il grafo

Esercizio in corso

Exercise 3: Visualizza i nodi per grado Exercise 4: Qual è la distribuzione della centralità?Exercise 5: Chi è importante nella conversazione?Exercise 6: Rappresentare i vertici importanti Exercise 7: Creare un grafo delle menzioni Exercise 8: Confrontare i grafi di mention e retweet Exercise 9: Assortatività e reciprocità Exercise 10: Scoprire chi parla con chi Exercise 11: Individuare le comunità Exercise 12: Confrontare gli algoritmi di individuazione delle comunità Exercise 13: Visualizzare le comunità

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