Dasar-dasar API NetworkX, menggunakan jaringan Twitter

Agar Anda siap menggunakan API NetworkX, kita akan meninjau beberapa fungsi dasar yang memungkinkan Anda melakukan kueri pada jaringan Twitter yang telah dimuat sebelumnya untuk Anda dan tersedia di IPython Shell sebagai T. Jaringan Twitter ini berasal dari KONECT, dan menampilkan cuplikan dari sebagian pengguna Twitter. Ini adalah jaringan Twitter yang dianonimkan dengan metadata.

Sekarang Anda akan menggunakan API NetworkX untuk mengeksplorasi beberapa properti dasar jaringan, dan Anda dianjurkan untuk bereksperimen dengan datanya di IPython Shell.

Tunggu hingga IPython Shell menunjukkan bahwa graf telah dimuat sebelumnya dengan nama variabel T (mewakili jaringan Twitter), lalu jawab pertanyaan berikut:

Berapakah ukuran graf T, tipe dari T.nodes(), dan struktur data dari elemen ketiga pada sisi terakhir yang terdaftar di T.edges(data=True)? Fungsi len() dan type() akan berguna di sini. Untuk mengakses entri terakhir dari T.edges(data=True), Anda dapat menggunakan list(T.edges(data=True))[-1].

Latihan ini adalah bagian dari kursus

Pengantar Analisis Jaringan di Python

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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: Pendahuluan tentang Jaringan Exercise 2: Apa itu jaringan?Exercise 3: Dasar-dasar API NetworkX, menggunakan jaringan Twitter

Latihan Saat Ini

Exercise 4: Gambar dasar jaringan menggunakan NetworkX Exercise 5: Kueri pada graf Exercise 6: Jenis grafik Exercise 7: Memeriksa status ber/arah pada sebuah graf Exercise 8: Menentukan bobot pada sisi (edges)Exercise 9: Memeriksa apakah ada self-loop dalam graf Exercise 10: Visualisasi jaringan Exercise 11: Visualisasi menggunakan plot Matriks Exercise 12: Visualisasi menggunakan plot Circos Exercise 13: Visualisasi menggunakan Arc plot

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: 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