net w ork anal y sis in r a tid y approach
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Net w ork anal y sis in R : A tid y approach N E TW OR K AN ALYSIS - PowerPoint PPT Presentation

Net w ork anal y sis in R : A tid y approach N E TW OR K AN ALYSIS IN TH E TIDYVE R SE Massimo Franceschet Prof . of Data Science , Uni v ersit y of Udine ( Ital y) NETWORK ANALYSIS IN THE TIDYVERSE NETWORK ANALYSIS IN THE TIDYVERSE NETWORK


  1. Net w ork anal y sis in R : A tid y approach N E TW OR K AN ALYSIS IN TH E TIDYVE R SE Massimo Franceschet Prof . of Data Science , Uni v ersit y of Udine ( Ital y)

  2. NETWORK ANALYSIS IN THE TIDYVERSE

  3. NETWORK ANALYSIS IN THE TIDYVERSE

  4. NETWORK ANALYSIS IN THE TIDYVERSE

  5. NETWORK ANALYSIS IN THE TIDYVERSE

  6. NETWORK ANALYSIS IN THE TIDYVERSE

  7. B u ilding the net w ork # load packages for network exploration library(readr) library(igraph) # read nodes and ties data into variables nodes <- read_csv("nodes.csv") ties <- read_csv("ties.csv") # build a network from data frames g <- graph_from_data_frame(d = ties, directed = FALSE, vertices = nodes) NETWORK ANALYSIS IN THE TIDYVERSE

  8. E x ploring the net w ork # explore the set of nodes and print the number of nodes V(g) vcount(g) # explore the set of ties and print the number of ties E(g) ecount(g) # add the name attribute "Madrid network" to the network and print it g$name <- "Madrid network" g$name # add node attribute id and print the node `id` attribute V(g)$id <- 1:vcount(g) # print the tie `weight` attribute E(g)$weight NETWORK ANALYSIS IN THE TIDYVERSE

  9. Let ' s start the in v estigation ! N E TW OR K AN ALYSIS IN TH E TIDYVE R SE

  10. Vis u ali z ing net w orks N E TW OR K AN ALYSIS IN TH E TIDYVE R SE Massimo Franceschet Prof . of Data Science , Uni v ersit y of Udine ( Ital y)

  11. ggraph () # load packages for data manipulation and visualization library{igraph) library(dplyr) library(ggplot2) library(ggraph) # visualize the network ggraph(g, layout = "with_kk") + geom_edge_link(aes(alpha = weight)) + geom_node_point() NETWORK ANALYSIS IN THE TIDYVERSE

  12. NETWORK ANALYSIS IN THE TIDYVERSE

  13. Let ' s practice ! N E TW OR K AN ALYSIS IN TH E TIDYVE R SE

  14. Centralit y meas u res N E TW OR K AN ALYSIS IN TH E TIDYVE R SE Massimo Franceschet Prof . of Data Science , Uni v ersit y of Udine ( Ital y)

  15. NETWORK ANALYSIS IN THE TIDYVERSE

  16. Node centralit y Which are the most important nodes in a net w ork ? Important w eb pages abo u t a certain topic In �u ential academic papers co v ering a gi v en iss u e Internet ro u ters w hose fail u re w o u ld greatl y a � ect net w ork connecti v it y NETWORK ANALYSIS IN THE TIDYVERSE

  17. NETWORK ANALYSIS IN THE TIDYVERSE

  18. Comp u ting degree # compute node degrees degree(g) Jamal Zougam Mohamed Bekkali Mohamed Chaoui 29 2 27 Vinay Kholy Suresh Kumar Mohamed Chedadi 10 10 7 Imad Eddin Barakat Abdelaziz Benyaich Abu Abderrahame 22 6 4 Omar Dhegayes Amer Azizi Abu Musad Alsakaoui 2 18 10 Mohamed Atta Ramzi Binalshibh Mohamed Belfatmi 10 10 11 Said Bahaji Galeb Kalaje Abderrahim Zbakh 11 16 15 NETWORK ANALYSIS IN THE TIDYVERSE

  19. NETWORK ANALYSIS IN THE TIDYVERSE

  20. Comp u ting strength # compute node strengths strength(g) Jamal Zougam Mohamed Bekkali Mohamed Chaoui 43 2 34 Vinay Kholy Suresh Kumar Mohamed Chedadi 10 10 7 Imad Eddin Barakat Abdelaziz Benyaich Abu Abderrahame 35 7 4 Omar Dhegayes Amer Azizi Abu Musad Alsakaoui 3 27 10 Mohamed Atta Ramzi Binalshibh Mohamed Belfatmi 12 14 19 Said Bahaji Galeb Kalaje Abderrahim Zbakh 17 21 15 NETWORK ANALYSIS IN THE TIDYVERSE

  21. Let ' s find the most central terrorists in the net w ork ! N E TW OR K AN ALYSIS IN TH E TIDYVE R SE

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