Menemukan kolaborator penting

Hampir selesai! Anda sekarang akan melihat kembali node-node penting. Di sini, Anda akan memanfaatkan fungsi degree_centrality() dan betweenness_centrality() di NetworkX untuk menghitung masing-masing skor centrality, lalu menggunakan informasi tersebut untuk menemukan "node penting". Dengan kata lain, tugas Anda dalam latihan ini adalah menemukan pengguna yang telah berkolaborasi dengan jumlah pengguna terbanyak.

Latihan ini adalah bagian dari kursus

Pengantar Analisis Jaringan di Python

Petunjuk latihan

Hitung degree centrality dari G. Simpan hasilnya sebagai deg_cent.
Hitung degree centrality maksimum. Karena deg_cent adalah sebuah dictionary, Anda harus menggunakan metode .values() untuk mendapatkan daftar nilainya sebelum menghitung degree centrality maksimum dengan max().
Identifikasi kolaborator paling produktif menggunakan list comprehension:
- Lakukan iterasi atas dictionary degree centrality deg_cent yang telah dihitung sebelumnya menggunakan metode .items(). Kondisi apa yang harus dipenuhi jika Anda ingin menemukan pengguna yang telah berkolaborasi dengan jumlah pengguna terbanyak? Petunjuk: Ini berkaitan dengan degree centrality maksimum.
Tekan 'Kirim Jawaban' untuk melihat siapa kolaborator paling produktif!

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Compute the degree centralities of G: deg_cent
deg_cent = ____

# Compute the maximum degree centrality: max_dc
max_dc = ____

# Find the user(s) that have collaborated the most: prolific_collaborators
prolific_collaborators = [n for n, dc in ____ if ____ == ____]

# Print the most prolific collaborator(s)
print(prolific_collaborators)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Analisis Jaringan di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, you'll be introduced to fundamental concepts in network analytics while exploring a real-world Twitter network dataset. You'll also learn about NetworkX, a library that allows you to manipulate, analyze, and model graph data. You'll learn about the different types of graphs and how to rationally visualize them.

Exercise 1: Introduction to Networks Exercise 2: What is a network?Exercise 3: Basics of NetworkX API, using Twitter network Exercise 4: Basic drawing of a network using NetworkX Exercise 5: Queries on a graph Exercise 6: Types of graphs Exercise 7: Checking the un/directed status of a graph Exercise 8: Specifying a weight on edges Exercise 9: Checking whether there are self-loops in the graph Exercise 10: Network visualization Exercise 11: Visualizing using Matrix plots Exercise 12: Visualizing using Circos plots Exercise 13: Visualizing using Arc plots

You'll learn about ways to identify nodes that are important in a network. In doing so, you'll be introduced to more advanced concepts in network analysis as well as the basics of path-finding algorithms. The chapter concludes with a deep dive into the Twitter network dataset which will reinforce the concepts you've learned, such as degree centrality and betweenness centrality.

Exercise 1: Degree centrality Exercise 2: Compute number of neighbors for each node Exercise 3: Compute degree distribution Exercise 4: Degree centrality distribution Exercise 5: Graph algorithms Exercise 6: Shortest Path I Exercise 7: Shortest Path II Exercise 8: Shortest Path III Exercise 9: Betweenness centrality Exercise 10: NetworkX betweenness centrality on a social network Exercise 11: Deep dive - Twitter network Exercise 12: Deep dive - Twitter network part II

This chapter is all about finding interesting structures within network data. You'll learn about essential concepts such as cliques, communities, and subgraphs, which will leverage all of the skills you acquired in Chapter 2. By the end of this chapter, you'll be ready to apply the concepts you've learned to a real-world case study.

Exercise 1: Communities & cliques Exercise 2: Identifying triangle relationships Exercise 3: Finding nodes involved in triangles Exercise 4: Finding open triangles Exercise 5: Maximal cliques Exercise 6: Finding all maximal cliques of size "n"Exercise 7: Subgraphs Exercise 8: Subgraphs I Exercise 9: Subgraphs II

In this final chapter of the course, you'll consolidate everything you've learned through an in-depth case study of GitHub collaborator network data. This is a great example of real-world social network data, and your newly acquired skills will be fully tested. By the end of this chapter, you'll have developed your very own recommendation system to connect GitHub users who should collaborate together.

Exercise 1: Studi kasus!Exercise 2: Mencirikan jaringan (I)Exercise 3: Mencirikan jaringan (II)Exercise 4: Mencirikan jaringan (III)Exercise 5: Studi kasus bagian II: Visualisasi Exercise 6: Plot Matrix Exercise 7: Plot Arc Exercise 8: Plot Circos Exercise 9: Studi kasus bagian III: Clique Exercise 10: Menemukan clique (I)Exercise 11: Menemukan clique (II)Exercise 12: Studi Kasus Bagian IV: Tugas Akhir Exercise 13: Menemukan kolaborator penting

Latihan Saat Ini

Exercise 14: Mencirikan komunitas penyuntingan Exercise 15: Merekomendasikan rekan editor yang belum pernah mengedit bersama Exercise 16: Pemikiran akhir