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.

Introduction to Networks

What is a network?

Basics of NetworkX API, using Twitter network

Basic drawing of a network using NetworkX

Queries on a graph

Types of graphs

Checking the un/directed status of a graph

Specifying a weight on edges

Checking whether there are self-loops in the graph

Network visualization

Visualizing using Matrix plots

Visualizing using Circos plots

Visualizing using Arc plots

Introduction to networks

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.

Degree centrality

Compute number of neighbors for each node

Compute degree distribution

Degree centrality distribution

Graph algorithms

Shortest Path I

Shortest Path II

Shortest Path III

Betweenness centrality

NetworkX betweenness centrality on a social network

Deep dive - Twitter network

Deep dive - Twitter network part II

Important nodes

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.

Communities & cliques

Identifying triangle relationships

Finding nodes involved in triangles

Finding open triangles

Maximal cliques

Finding all maximal cliques of size "n"

Subgraphs

Subgraphs I

Subgraphs II

Structures

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.

Studi kasus!

Mencirikan jaringan (I)

Mencirikan jaringan (II)

Mencirikan jaringan (III)

Studi kasus bagian II: Visualisasi

Plot Matrix

Plot Arc

Plot Circos

Studi kasus bagian III: Clique

Menemukan clique (I)

Menemukan clique (II)

Studi Kasus Bagian IV: Tugas Akhir

Menemukan kolaborator penting

Mencirikan komunitas penyuntingan

Merekomendasikan rekan editor yang belum pernah mengedit bersama

Pemikiran akhir

Bringing it all together

Twitter network

GitHub users

Dari jejaring sosial daring seperti Facebook dan Twitter hingga jaringan transportasi seperti sistem berbagi sepeda, jaringan ada di mana-mana—dan mengetahui cara menganalisisnya akan membuka dunia kemungkinan baru bagi Anda sebagai data scientist. Kursus ini akan membekali Anda dengan keterampilan untuk menganalisis, memvisualisasikan, dan memahami jaringan. Anda akan menerapkan konsep yang dipelajari pada data jaringan dunia nyata menggunakan pustaka NetworkX yang andal. Dengan pengetahuan yang diperoleh dalam kursus ini, Anda akan mengembangkan cara berpikir berbasis jaringan dan mampu melihat data Anda dari sudut pandang yang baru.

Python Toolbox

Diajarkan oleh Eric Ma dari Runstadler Lab di MIT, kursus ini mengajarkan analisis jaringan sosial.

Pengantar Analisis Jaringan di Python

Kursus ini akan membekali Anda dengan keterampilan untuk menganalisis, memvisualisasikan, dan memahami jaringan menggunakan perpustakaan NetworkX.

Studi kasus bagian II: Visualisasi

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