Vérifier si un graphe est orienté ou non orienté

Dans la vidéo, Eric vous a présenté différents types de graphes. À quel type pensez-vous que correspond le réseau Twitter avec lequel vous avez travaillé ? Utilisez la fonction intégrée de Python type() dans l’IPython Shell pour le découvrir. Comme précédemment, le réseau a été préchargé sous le nom T.

Parmi les quatre choix ci-dessous, lequel correspond au type de graphe de T ?

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<cours>Introduction à l’analyse de réseaux en Python</cours>

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<cours>Introduction à l’analyse de réseaux en Python</cours>

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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 aux réseaux Exercise 2: Qu’est-ce qu’un réseau ?Exercise 3: Bases de l’API NetworkX avec un réseau Twitter Exercise 4: Tracé simple d’un réseau avec NetworkX Exercise 5: Requêtes sur un graphe Exercise 6: Types de graphes Exercise 7: Vérifier si un graphe est orienté ou non orienté

Exercice actuel

Exercise 8: Spécifier un poids sur les arêtes Exercise 9: Vérifier la présence de boucles (self-loops) dans le graphe Exercise 10: Visualisation de réseau Exercise 11: Visualiser avec des graphiques matriciels Exercise 12: Visualiser avec des graphiques Circos Exercise 13: Visualiser avec des 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: 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