Hitung distribusi derajat

Jumlah tetangga yang dimiliki sebuah node disebut "derajat" (degree), dan kita dapat menghitung distribusi derajat di seluruh graf. Pada latihan ini, tugas Anda adalah menghitung distribusi derajat pada T.

Latihan ini adalah bagian dari kursus

Pengantar Analisis Jaringan di Python

Petunjuk latihan

Gunakan list comprehension bersama metode .neighbors(n) untuk memperoleh derajat setiap node. Hasilnya harus berupa daftar bilangan bulat.
- Gunakan n sebagai variabel iterator Anda.
- Ekspresi keluaran dari list comprehension Anda harus berupa jumlah tetangga yang dimiliki node n—yaitu derajatnya. Gunakan fungsi len() dan list() bersama metode .neighbors() untuk menghitungnya.
- Iterable dalam list comprehension Anda adalah semua node di T, diakses menggunakan metode .nodes().
Cetak derajat-derajat tersebut.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Compute the degree of every node: degrees
degrees = [____]

# Print the degrees

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: Kesentralan derajat Exercise 2: Hitung jumlah tetangga untuk setiap node Exercise 3: Hitung distribusi derajat

Latihan Saat Ini

Exercise 4: Distribusi degree centrality Exercise 5: Algoritme graf Exercise 6: Shortest Path I Exercise 7: Jalur Terpendek II Exercise 8: Shortest Path III Exercise 9: Keterpusatan betweenness Exercise 10: Betweenness centrality NetworkX pada jejaring sosial Exercise 11: Pendalaman - Jaringan Twitter Exercise 12: Pendalaman - Jaringan Twitter bagian 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: Case study!Exercise 2: Characterizing the network (I)Exercise 3: Characterizing the network (II)Exercise 4: Characterizing the network (III)Exercise 5: Case study part II: Visualization Exercise 6: Matrix plot Exercise 7: Arc plot Exercise 8: Circos plot Exercise 9: Case study part III: Cliques Exercise 10: Finding cliques (I)Exercise 11: Finding cliques (II)Exercise 12: Case Study Part IV: Final Tasks Exercise 13: Finding important collaborators Exercise 14: Characterizing editing communities Exercise 15: Recommending co-editors who have yet to edit together Exercise 16: Final thoughts