Gewogen centraliteitsmaten

Een andere veelgebruikte maat voor belangrijke knopen is centraliteit. Er zijn verschillende manieren om centraliteit te meten, maar in deze les kijken we naar twee maten: eigen-centraliteit en closeness. Eigen-centraliteit is al eerder behandeld; closeness is een andere manier om centraliteit te beoordelen. Daarbij kijk je hoe dicht een knoop bij alle andere knopen ligt. In eerdere lessen hebben we niet expliciet onderscheid gemaakt tussen gewogen en ongewogen versies van centraliteit. In deze les berekenen we beide varianten en kijken we of dit verschil uitmaakt.

Verwacht je in onderstaand voorbeeld telkens dezelfde knoop te zien? Wat denk je dat het grootste verschil zal zijn tussen de maten, of tussen de gewogen en ongewogen varianten?

Deze oefening maakt deel uit van de cursus

Casestudies: netwerkanalyse in R

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Casestudies: netwerkanalyse 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: Een graaf maken vanuit ruwe data Exercise 2: Grafen maken voor verschillende gebruikerstypen Exercise 3: Vergelijk grafen van verschillende gebruikerstypes Exercise 4: Vergelijk graafafstand met geografische afstand Exercise 5: Afstanden van abonnees vs. niet-abonnees vergelijken Exercise 6: Meest bereisde vertrek- en aankomststations Exercise 7: Meest bereisde vertrek- en aankomststations met gewichten Exercise 8: Visualiseer centrale knooppunten Exercise 9: Gewogen centraliteitsmaten

Huidige oefening

Exercise 10: Connectiviteit Exercise 11: Vind de minimale snede 1 Exercise 12: Vind de minimum cut 2 Exercise 13: Ongerichte clusterrandomisaties Exercise 14: Gewogen clustering-randomisaties

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