Memplot simpul-simpul penting

Terakhir, kita akan memvisualisasikan graf dengan ukuran simpul sesuai dengan sentralitas. Namun, kita sudah tahu grafnya sangat timpang, sehingga kemungkinan akan muncul banyak noise visual. Karena itu, selanjutnya kita ingin melihat seberapa terhubung simpul-simpul yang paling sentral. Kita dapat melakukannya dengan membuat subgraf berdasarkan kriteria sentralitas. Ini adalah teknik penting saat bekerja dengan graf berukuran besar. Nanti, kita akan melihat metode visualisasi alternatif, tetapi teknik kuat lainnya adalah memvisualisasikan subgraf.

Graf, retweet_graph, betweenness simpulnya, retweet_btw, dan kuantil betweenness 0,99, betweenness_q99, telah tersedia.

Latihan ini adalah bagian dari kursus

Studi Kasus: Analisis Jaringan di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Transform betweenness: add 2 then take natural log
transformed_btw <- log(___ + 2)

# Make transformed_btw the size attribute of the vertices
V(___)$ <- ___

# Plot the graph
plot(
  ___, vertex.label = NA, edge.arrow.width = 0.2,
  edge.arrow.size = 0.0, vertex.color = "red"
)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Studi Kasus: Analisis Jaringan di R

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus 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: 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: Membuat grafik retweet Anda Exercise 2: Visualisasikan grafik Exercise 3: Visualisasikan simpul berdasarkan derajat Exercise 4: Seperti apa distribusi centrality?Exercise 5: Siapa yang penting dalam percakapan?Exercise 6: Memplot simpul-simpul penting

Latihan Saat Ini

Exercise 7: Membangun graf sebutan Exercise 8: Membandingkan graf mention dan retweet Exercise 9: Asortativitas dan resiprositas Exercise 10: Menemukan siapa berbicara dengan siapa Exercise 11: Menemukan komunitas Exercise 12: Membandingkan algoritme komunitas Exercise 13: Memvisualisasikan komunitas

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