Misure di centralità pesate

Un'altra misura comune per identificare i vertici importanti è la centralità. Ci sono vari modi per misurarla, ma in questa lezione considereremo due metriche: eigen centrality e closeness. Anche se eigen centrality è già stata trattata, closeness è un altro modo per valutare la centralità: misura quanto ogni vertice sia vicino a tutti gli altri. Nelle lezioni precedenti non abbiamo considerato in modo esplicito le versioni pesate rispetto a quelle non pesate della centralità. In questa lezione calcoleremo entrambe le versioni e vedremo se qualcosa cambia nei risultati.

Nell'esempio qui sotto, ti aspetti di vedere ogni volta lo stesso vertice? Secondo te qual è la differenza più grande tra le metriche o tra le versioni pesate e non pesate?

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Casi di studio: analisi di reti in R

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# This calculates weighted eigen-centrality 
ec_weight <- eigen_centrality(trip_g_simp, directed = TRUE)$vector

# Calculate unweighted eigen-centrality 
ec_unweight <- eigen_centrality(___, directed = ___, weights = ___)$vector

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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: 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: Creare il nostro grafo a partire da dati grezzi Exercise 2: Creare grafi per diversi tipi di utenti Exercise 3: Confronta i grafi per diversi tipi di utenti Exercise 4: Confronta distanza nel grafo vs. distanza geografica Exercise 5: Confronta le distanze: abbonati vs. non abbonati Exercise 6: Stazioni più percorse in entrata e in uscita Exercise 7: Stazioni più frequentate in uscita e in entrata con pesi Exercise 8: Visualizza i vertici centrali Exercise 9: Misure di centralità pesate

Esercizio in corso

Exercise 10: Connettività Exercise 11: Trova il taglio minimo 1 Exercise 12: Trova il taglio minimo 2 Exercise 13: Randomizzazioni del clustering non pesato Exercise 14: Randomizzazioni del clustering pesato

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