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.

Exploring your data set

Finding Dyads and Triads

Clustering and Reciprocity

Important Products

What Makes an Important Product?

Exploring temporal structure

Metrics through time

Plotting Metrics Over Time

Exploring graphs through 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.

Creating your retweet graph

Visualize the graph

Visualize nodes by degree

What's the distribution of centrality?

Who is important in the conversation?

Plotting important vertices

Building a mentions graph

Comparing mention and retweet graph

Assortativity and reciprocity

Finding who is talking to whom

Finding communities

Comparing community algorithms

Visualizing the communities

How do people talk about R on Twitter?

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.

Membuat graf dari data mentah

Membuat Graf untuk Berbagai Jenis Pengguna

Bandingkan Graf dari Jenis Pengguna yang Berbeda

Bandingkan jarak graf vs. jarak geografis

Bandingkan Jarak Pelanggan Berlangganan vs. Non-Berlangganan

Stasiun dengan Arus Masuk dan Keluar Terbanyak

Stasiun Paling Sering Dikunjungi Masuk dan Keluar dengan Bobot

Visualisasikan simpul pusat

Ukuran Sentralitas Berbobot

Konektivitas

Cari minimum cut 1

Temukan minimum cut 2

Pengacakan Klaster Tanpa Bobot

Randomisasi Klaster Berbobot

Bike sharing in Chicago

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.

Other packages for plotting graphs!

ggnet Basics

ggnetwork Basics

More ggnet Plotting Options

More ggnetwork Plotting Options

Interactive visualizations

Interactive plots with ggiraph

Interactive javascript plots

Alternative visualizations

Alternative ways to visualize a graph: Hive plots

BioFabric as an HTML widget

Plotting graphs on a map

Other ways to visualize graph data

Amazon graph

Amazon purchase graph over time

Twitter retweet graph

Twitter mention graph

Bike sharing data

Sekarang setelah Anda memahami dasar-dasar analisis jaringan, saatnya melihat cara menerapkan konsep tersebut pada himpunan data besar di dunia nyata. Anda akan mengerjakan tiga studi kasus berbeda, masing-masing membangun dari pekerjaan sebelumnya. Studi kasus ini menggunakan jenis data yang akan Anda jumpai baik di lingkungan akademik maupun industri. Kita akan mengeksplorasi sejumlah tantangan komputasi dan visualisasi yang mungkin Anda hadapi serta cara mengatasinya. Pengetahuan Anda tentang igraph akan terus berkembang, dan kita juga akan memanfaatkan pustaka visualisasi lain yang membantu membawa visualisasi Anda ke web.

Network Analysis in R

Terapkan konsep dasar analisis jaringan pada dataset besar dunia nyata dalam 4 studi kasus berbeda.

Studi Kasus: Analisis Jaringan di R

Terapkan konsep dasar analisis jaringan pada dataset dunia nyata yang besar dalam 4 studi kasus yang berbeda.

Membuat graf dari data mentah

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