Menemukan siapa berbicara dengan siapa

Ingat dari pelajaran pertama bahwa clique adalah subgraf lengkap di dalam graf tak berarah yang lebih besar. Saat kita melihat struktur clique dari graf percakapan di Twitter, hal itu memberi tahu kita siapa yang berbicara dengan siapa. Salah satu cara kita dapat menggunakan informasi ini adalah untuk melihat siapa yang mungkin tertarik berbicara dengan orang lain. Mudah dibayangkan bagaimana informasi dasar ini dapat digunakan untuk membangun model pengguna yang disarankan untuk diikuti atau diajak berinteraksi.

Latihan ini adalah bagian dari kursus

Studi Kasus: Analisis Jaringan di R

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Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Get size 3 cliques
list_of_clique_vertices <- cliques(___, min = ___, max = ___)

# Loop over cliques, getting IDs
clique_ids <- lapply(___, as_ids)

# See the result
clique_ids

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Latihan ini adalah bagian dari kursus

Studi Kasus: Analisis Jaringan di R

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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 Exercise 7: Membangun graf sebutan Exercise 8: Membandingkan graf mention dan retweet Exercise 9: Asortativitas dan resiprositas Exercise 10: Menemukan siapa berbicara dengan siapa

Latihan Saat Ini

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