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Social Network Analysis in R

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Brief demonstration of social network analysis in R using the package igraph. Based on a presentation by Drew Conway at a NYC R Statistical Programming Meetup.

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Social Network Analysis in R

  1. 1. Social Network Analysis in R Ian M. Cook (based on a presentation by Drew Conway at a NYC R Statistical Programming Meetup) June 23, 2011
  2. 2. Social Network Analysis in R June 23, 2011 Loading Social Network Data
  3. 3. Loading An Edgelist <ul><li>library(igraph) edgelist <- read.table(&quot;C:edgelist.txt&quot;,header=T) igraph <- graph.data.frame( edgelist, directed=TRUE) </li></ul><ul><li>directed vs. undirected </li></ul><ul><li>SNA data also can also be represented as a sociomatrix </li></ul>Social Network Analysis in R June 23, 2011
  4. 4. Social Network Analysis in R June 23, 2011 Visualizing Social Networks
  5. 5. Tcl/Tk Network Graph <ul><li>tkplot(igraph, layout=layout.fruchterman.reingold) </li></ul>Social Network Analysis in R June 23, 2011
  6. 6. Social Network Analysis in R June 23, 2011 Finding Key Actors
  7. 7. Centrality Measures <ul><li>Metrics of actor centrality include: </li></ul><ul><li>degree (number of connections) </li></ul><ul><li>betweenness (number of shortest paths an actor is on) </li></ul><ul><li>closeness (relative distance to all other actors) </li></ul><ul><li>eigenvector centrality (leading eigenvector of sociomatrix) </li></ul>Social Network Analysis in R June 23, 2011
  8. 8. Calculating Centrality Measures <ul><li>metrics <- data.frame( deg=degree(igraph), # degree bet=betweenness(igraph), # betweenness clo=closeness(igraph), # closeness eig=evcent(igraph)$vector, # eig.cent. cor=graph.coreness(igraph) # coreness ) </li></ul>Social Network Analysis in R June 23, 2011
  9. 9. Plot Eigenvector Centrality on Betweenness <ul><li>One method is to plot/regress eigenvector centrality on betweenness and examine the residuals. </li></ul><ul><ul><ul><li>An actor with high betweenness and low eigenvector centrality may be an important gatekeeper to a central actor. </li></ul></ul></ul><ul><ul><ul><li>An actor with low betweeness and high eigenvector centrality may have unique access to central actors. </li></ul></ul></ul>Social Network Analysis in R June 23, 2011
  10. 10. Visualizing with ggplot2 <ul><li>library(ggplot2) ggplot( metrics, aes(x=bet,y=eig, label=rownames(metrics), colour=res,size=abs(res)) )+ xlab(&quot;Betweenness Centrality&quot;)+ ylab(&quot;Eigenvector Centrality&quot;)+ geom_text()+ opts(title=&quot;Key Actor Analysis for Hartford Drug Users&quot;) </li></ul>Social Network Analysis in R June 23, 2011

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