Trovare le clique (I)

Ora metterai in pratica la ricerca delle clique in G. Ricorda che le clique sono "gruppi di nodi completamente connessi tra loro", mentre una clique massimale è una clique che non può essere estesa aggiungendo un altro nodo nel grafo.

Questo esercizio fa parte del corso

Introduzione all'analisi delle reti in Python

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Istruzioni dell'esercizio

Conta il numero di clique massimali presenti nel grafo e stampalo.
- Usa la funzione nx.find_cliques() di G per trovare le clique massimali.
- La funzione nx.find_cliques() restituisce un oggetto generatore. Per contare il numero di clique massimali, devi prima convertirlo in una lista con list() e poi usare la funzione len(). Metti tutto dentro una print() per stamparlo.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Calculate the maximal cliques in G: cliques
cliques = ____

# Count and print the number of maximal cliques in G
print(____)

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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: 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: Caso di studio!Exercise 2: Caratterizzare la rete (I)Exercise 3: Caratterizzare la rete (II)Exercise 4: Caratterizzare la rete (III)Exercise 5: Studio di caso parte II: Visualizzazione Exercise 6: Grafico Matrix Exercise 7: Arc plot Exercise 8: Grafico Circos Exercise 9: Case study parte III: Clique Exercise 10: Trovare le clique (I)

Esercizio in corso

Exercise 11: Trovare le clique (II)Exercise 12: Studio di caso Parte IV: Attività finali Exercise 13: Trovare i collaboratori importanti Exercise 14: Caratterizzare le comunità di editing Exercise 15: Consigliare co-editor che non hanno ancora modificato insieme Exercise 16: Considerazioni finali