Calcola la distribuzione dei gradi

Il numero di vicini che un nodo ha si chiama "grado" ed è possibile calcolare la distribuzione dei gradi su tutto il grafo. In questo esercizio, il tuo compito è calcolare la distribuzione dei gradi su T.

Questo esercizio fa parte del corso

Introduzione all'analisi delle reti in Python

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Istruzioni dell'esercizio

Usa una list comprehension insieme al metodo .neighbors(n) per ottenere il grado di ogni nodo. Il risultato dovrebbe essere un elenco di interi.
- Usa n come tua variabile iteratore.
- L'espressione in output della tua list comprehension dovrebbe essere il numero di vicini che ha il nodo n, cioè il suo grado. Usa le funzioni len() e list() insieme al metodo .neighbors() per calcolarlo.
- L'iterabile nella tua list comprehension è l'insieme di tutti i nodi in T, a cui si accede con il metodo .nodes().
Stampa i gradi.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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

# Print the degrees

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Questo esercizio fa parte del corso

Introduzione all'analisi delle reti in Python

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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: Centralità di grado Exercise 2: Calcola il numero di vicini per ogni nodo Exercise 3: Calcola la distribuzione dei gradi

Esercizio in corso

Exercise 4: Distribuzione della degree centrality Exercise 5: Algoritmi sui grafi Exercise 6: Shortest Path I Exercise 7: Shortest Path II Exercise 8: Shortest Path III Exercise 9: Betweenness centrality Exercise 10: Betweenness centrality di NetworkX su un social network Exercise 11: Approfondimento - Rete di Twitter Exercise 12: Approfondimento - Rete di Twitter parte 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