Het netwerk karakteriseren (I)

Laten we beginnen met een basiskarakterisering van het netwerk door te kijken naar het aantal knopen en randen. Het netwerk is al voor je ingeladen als G en staat klaar voor verkenning in de IPython Shell. Jouw taak in deze oefening is vast te stellen hoeveel knopen en randen er in het netwerk aanwezig zijn. Je kunt de functies len(G.nodes()) en len(G.edges()) gebruiken om respectievelijk het aantal knopen en randen te berekenen.

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Introductie tot netwerkanalyse in Python

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Introductie tot netwerkanalyse 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: Casestudy!Exercise 2: Het netwerk karakteriseren (I)

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Exercise 3: Het netwerk karakteriseren (II)Exercise 4: Het netwerk karakteriseren (III)Exercise 5: Casestudy deel II: Visualisatie Exercise 6: Matrix-plot Exercise 7: Arc-plot Exercise 8: Circos-plot Exercise 9: Casestudy deel III: Cliques Exercise 10: Cliques vinden (I)Exercise 11: Kliekens zoeken (II)Exercise 12: Casestudy deel IV: laatste taken Exercise 13: Belangrijke samenwerkers vinden Exercise 14: Bewerkingsgemeenschappen karakteriseren Exercise 15: Co-editors aanbevelen die nog niet samen hebben bewerkt Exercise 16: Tot slot