Trovare diadi e triadi

Ragioniamo meglio su cosa possiamo imparare dai censimenti diadi e triadi dell’intero grafo. Poiché ci interessa capire come gli articoli vengono acquistati insieme e se gli acquisti sono reciproci oppure no, i censimenti diadi e triadi offrono un utile primo sguardo. Un censimento delle diadi ci dirà quanti articoli sono acquistati in modo reciproco vs. asimmetrico. Il censimento delle triadi ci indicherà quali articoli potrebbero essere importanti, in particolare i pattern 4, 9 e 12. Tutti questi hanno un vertice con 2 archi in uscita e nessun arco in entrata. Anche la densità degli archi dovrebbe dare informazioni in linea con ciò che ci aspettiamo per il clustering del grafo.

Il grafo di co-acquisto di Amazon, amzn_g, è disponibile.

Questo esercizio fa parte del corso

Casi di studio: analisi di reti in R

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Istruzioni dell'esercizio

Esplora amzn_g.

Esegui un censimento delle diadi usando la funzione dyad_census().
Esegui un censimento delle triadi usando la funzione triad_census().
Trova la densità degli archi usando la funzione edge_density().

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# The graph, amzn_g, is available
amzn_g

# Perform dyad census
___

# Perform triad census
___

# Find the edge density
___

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Casi di studio: analisi di reti in R

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In this chapter you'll explore a subset of an Amazon purchase graph. You'll build on what you've already learned, finding important products and discovering what drives purchases. You'll also examine how graphs can change through time by looking at the graph during different time periods.

Exercise 1: Esplora il tuo insieme di dati Exercise 2: Trovare diadi e triadi

Esercizio in corso

Exercise 3: Clustering e reciprocità Exercise 4: Prodotti importanti Exercise 5: Cosa rende un prodotto importante?Exercise 6: Esplorare la struttura temporale Exercise 7: Metriche nel tempo Exercise 8: Rappresentare le metriche nel tempo

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Exercise 1: Creating your retweet graph Exercise 2: Visualize the graph Exercise 3: Visualize nodes by degree Exercise 4: What's the distribution of centrality?Exercise 5: Who is important in the conversation?Exercise 6: Plotting important vertices Exercise 7: Building a mentions graph Exercise 8: Comparing mention and retweet graph Exercise 9: Assortativity and reciprocity Exercise 10: Finding who is talking to whom Exercise 11: Finding communities Exercise 12: Comparing community algorithms Exercise 13: Visualizing the communities

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Exercise 1: Creating our graph from raw data Exercise 2: Making Graphs of Different User Types Exercise 3: Compare Graphs of Different User Types Exercise 4: Compare graph distance vs. geographic distance Exercise 5: Compare Subscriber vs. Non-Subscriber Distances Exercise 6: Most Traveled To and From Stations Exercise 7: Most Traveled To and From Stations with Weights Exercise 8: Visualize central vertices Exercise 9: Weighted Measures of Centrality Exercise 10: Connectivity Exercise 11: Find the minimum cut 1 Exercise 12: Find the minimum cut 2 Exercise 13: Unweighted Clustering Randomizations Exercise 14: Weighted Clustering Randomizations

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Exercise 1: Other packages for plotting graphs!Exercise 2: ggnet Basics Exercise 3: ggnetwork Basics Exercise 4: More ggnet Plotting Options Exercise 5: More ggnetwork Plotting Options Exercise 6: Interactive visualizations Exercise 7: Interactive plots with ggiraph Exercise 8: Interactive javascript plots Exercise 9: Alternative visualizations Exercise 10: Alternative ways to visualize a graph: Hive plots Exercise 11: BioFabric as an HTML widget Exercise 12: Plotting graphs on a map