Verificare se un grafo è non diretto/diretto

Nel video, Eric ti ha descritto diversi tipi di grafi. A quale tipo pensi che corrisponda la rete di Twitter con cui hai lavorato? Usa la funzione integrata di Python type() nell'IPython Shell per scoprirlo. La rete, come prima, è stata precaricata come T.

Tra le quattro opzioni qui sotto, quale corrisponde al tipo di grafo di T?

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Introduzione all'analisi delle reti in Python

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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: Introduzione alle reti Exercise 2: Che cos'è una rete?Exercise 3: Basi della NetworkX API con una rete di Twitter Exercise 4: Disegno di base di una rete con NetworkX Exercise 5: Query su un grafo Exercise 6: Tipi di grafi Exercise 7: Verificare se un grafo è non diretto/diretto

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Exercise 8: Specificare un peso sugli archi Exercise 9: Verificare la presenza di autoanelli nel grafo Exercise 10: Visualizzazione delle reti Exercise 11: Visualizzazione con grafici a matrice Exercise 12: Visualizzare con i grafici Circos Exercise 13: Visualizzare con gli 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