Ukuran Sentralitas Berbobot

Ukuran umum lain untuk mengidentifikasi simpul penting adalah sentralitas. Ada beberapa cara untuk mengukurnya, namun untuk pelajaran ini kita akan membahas dua metrik: eigen centrality dan closeness. Eigen centrality telah dibahas sebelumnya, sementara closeness adalah cara lain untuk menilai sentralitas. Metrik ini melihat seberapa dekat suatu simpul terhadap semua simpul lainnya. Pada pelajaran sebelumnya, kita belum secara eksplisit membedakan versi berbobot dan tak berbobot dari sentralitas. Dalam pelajaran ini, kita akan menghitung keduanya—berbobot dan tak berbobot—dan melihat apakah ada perbedaan hasilnya.

Pada contoh di bawah, apakah Anda memperkirakan simpul yang sama akan muncul setiap kali? Menurut Anda, apa perbedaan terbesar antara metrik atau antara versi berbobot dan tak berbobot?

Latihan ini adalah bagian dari kursus

Studi Kasus: Analisis Jaringan di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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

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: 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: Membuat graf dari data mentah Exercise 2: Membuat Graf untuk Berbagai Jenis Pengguna Exercise 3: Bandingkan Graf dari Jenis Pengguna yang Berbeda Exercise 4: Bandingkan jarak graf vs. jarak geografis Exercise 5: Bandingkan Jarak Pelanggan Berlangganan vs. Non-Berlangganan Exercise 6: Stasiun dengan Arus Masuk dan Keluar Terbanyak Exercise 7: Stasiun Paling Sering Dikunjungi Masuk dan Keluar dengan Bobot Exercise 8: Visualisasikan simpul pusat Exercise 9: Ukuran Sentralitas Berbobot

Latihan Saat Ini

Exercise 10: Konektivitas Exercise 11: Cari minimum cut 1 Exercise 12: Temukan minimum cut 2 Exercise 13: Pengacakan Klaster Tanpa Bobot Exercise 14: Randomisasi Klaster Berbobot

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