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.

Caso di studio!

Caratterizzare la rete (I)

Caratterizzare la rete (II)

Caratterizzare la rete (III)

Studio di caso parte II: Visualizzazione

Grafico Matrix

Arc plot

Grafico Circos

Case study parte III: Clique

Trovare le clique (I)

Trovare le clique (II)

Studio di caso Parte IV: Attività finali

Trovare i collaboratori importanti

Caratterizzare le comunità di editing

Consigliare co-editor che non hanno ancora modificato insieme

Considerazioni finali

Bringing it all together

Twitter network

GitHub users

Dai social network online come Facebook e Twitter alle reti di trasporto come i sistemi di bike sharing, le reti sono ovunque—e saperle analizzare ti aprirà un mondo di nuove possibilità come data scientist. Questo corso ti fornirà le competenze per analizzare, visualizzare e interpretare le reti. Metterai in pratica i concetti appresi su dati di rete reali usando la potente libreria NetworkX. Con le conoscenze acquisite in questo corso, svilupperai il tuo modo di pensare per reti e saprai guardare i tuoi dati con una prospettiva nuova.

Python Toolbox

Corso di Eric Ma del Runstadler Lab al MIT: impara ad analizzare e visualizzare reti come Facebook e Twitter.

Introduzione all'analisi delle reti in Python

Questo corso ti darà le competenze per analizzare, visualizzare e capire le reti usando la libreria NetworkX.

Case study parte III: Clique

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