Trovare tutte le clique massimali di dimensione "n"

Ora che hai esplorato i triangoli (e i triangoli aperti), passiamo al concetto di clique massimali. Le clique massimali sono clique che non possono essere estese aggiungendo un arco adiacente e sono una proprietà utile del grafo per individuare le comunità. NetworkX fornisce una funzione che ti permette di identificare i nodi coinvolti in ogni clique massimale in un grafo: nx.find_cliques(G). Fai qualche prova con la funzione usandola su T nella IPython Shell, poi prova a rispondere all'esercizio.

Questo esercizio fa parte del corso

Introduzione all'analisi delle reti in Python

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Istruzioni dell'esercizio

Scrivi una funzione maximal_cliques() che ha due parametri - G e size - e trova tutte le clique massimali di dimensione n.
- Nel ciclo for, itera su tutte le clique in G usando la funzione nx.find_cliques().
- Se la clique corrente ha dimensione size, aggiungila alla lista mcs.
Usa un'istruzione assert e la tua funzione maximal_cliques() per verificare che ci siano 33 clique massimali di dimensione 3 nel grafo T.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Define maximal_cliques()
def ____:
    """
    Finds all maximal cliques in graph `G` that are of size `size`.
    """
    mcs = []
    for clique in ____:
        if ____ == ____:
            ____
    return mcs

# Check that there are 33 maximal cliques of size 3 in the graph T
assert ____ == ____

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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: Comunità e clique Exercise 2: Identificare relazioni a triangolo Exercise 3: Trovare i nodi coinvolti in triangoli Exercise 4: Trovare triangoli aperti Exercise 5: Cliques massimali Exercise 6: Trovare tutte le clique massimali di dimensione "n"

Esercizio in corso

Exercise 7: Sottografi Exercise 8: Sottografi I Exercise 9: Sottografi 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