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Social Network Analysis in R

Social Network Analysis in R. Enabled Analytics: The vision. What is Social Network Analysis?. A way of mapping and measuring relationships Can be applied to many types of networks Provides visual inspection, as well as mathematical analysis

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Social Network Analysis in R

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  1. Social Network Analysis in R

  2. Enabled Analytics:The vision • What is Social Network Analysis? • A way of mapping and measuring relationships • Can be applied to many types of networks • Provides visual inspection, as well as mathematical analysis • A way to understand complex problems within a network of people or objects

  3. Enabled Analytics:The vision • Objectives • To identify important entities/people within a given network • To find people within a network that can influence other people’s behavior

  4. Enabled Analytics:The vision • Social Networks are Everywhere

  5. Enabled Analytics:The vision • Social Networks are Everywhere

  6. Enabled Analytics:The vision • The Mathematics: Graph Theory

  7. Enabled Analytics:The vision • Software • has many network analysis packages: igraph, SNA, S.N.O.R.T., tnet

  8. Enabled Analytics:The vision • Pitfalls • Not a one person job • The necessary computers are very expensive • The most basic measurements are computationally expensive • Sampling is virtually impossible • Data is not easy to obtain • Data can be very large

  9. Enabled Analytics:The vision • Ever Expanding Data

  10. Enabled Analytics:The vision • Ever Expanding Data

  11. Enabled Analytics:The vision • Metrics • Social Network Analysis metrics measure the importance of a particular node • Measures of “center” Mohamed Atta Zacarias Moussaoui Marwan Al-Shehhi Essid Sami Ben Khemais

  12. Enabled Analytics:The vision • Betweeness Centrality

  13. Enabled Analytics:The vision • Betweeness Centrality NawafAlhamzi Mohamed Atta Zacarias Moussaoui Hani Hanjour Essid Sami Ben Khemais

  14. Enabled Analytics:The vision • Closeness Centrality

  15. Enabled Analytics:The vision • Closeness Centrality • Special case: Graphs that are not connected

  16. Enabled Analytics:The vision • Closeness Centrality Mohamed Atta NawafAlhamzi Hani Hanjour Ramzi Bin al-Shibh Marwan Al-Shehhi

  17. Enabled Analytics:The vision • Custom Metrics

  18. Enabled Analytics:The vision • First Order Influence Centrality first.order.influence<-function(g,vids,weight=NULL,nodeweight=NULL){   result<-vector('numeric',length(vids)) if(is.null(nodeweight)==F){ nweight<-sum(nodeweight)   }else{ nweight<-vcount(g)   } for(z in1:length(vids)){       arcs<-incident(g,vids[z],'out') if(length(arcs)==0){ next       } if(is.null(weight)==F){ if(is.numeric(weight)==F){ arcweights<-sum(get.edge.attribute(g,weight,arcs))         }else{ arcweights<-sum(weight[arcs])         }       }else{ arcweights<-length(arcs)       }       result[z]<-arcweights/nweight     }   result }

  19. Enabled Analytics:The vision • Second Order Influence Centrality second.order.influence<-function(g,vids,weight=NULL,nodeweight=NULL){   result<-vector('numeric',length(vids)) if(is.null(nodeweight)==FALSE){ nweight<-sum(nodeweight,na.rm=TRUE)   }else{ nweight<-vcount(g)   }   inner<-function(g1,vids2,weight1=weight,n2weight=nweight){ inr<-vector('numeric',length(vids2)) for(z in1:length(vids2)){         arcs<-incident(g1,vids2[z],'out') if(length(arcs)==0){ next           } if(is.null(weight1)==F){ if(is.numeric(weight1)==F){ arcweights<-sum(get.edge.attribute(g1,weight1,arcs))           }else{ arcweights<-sum(weight1[arcs])           }         }else{ arcweights<-length(arcs)         } inr[z]<-arcweights/n2weight       } inr   } for(iin1:length(vids)){     hood<-neighbors(g,vids[i],'out') if(length(hood)==0){ next       }     result[i]<-sum(inner(g1=g,vids2=hood,weight1=weight,n2weight=nweight))   } result }

  20. Enabled Analytics:The vision • KPMG [Link] versus KSU

  21. Enabled Analytics:The vision • KPMG [Link] versus KSU

  22. Enabled Analytics:The vision • References • Ranking of Closeness Centrality for Large-Scale Social Networks, Kazuya Okamoto, Wei Chen, Xiang-Yang Li • Krebs, Valdis. "Uncloaking Terrorist Networks" First Monday [Online], Volume 7 Number 4 (1 April 2002) • Opsahl, T., Agneessens, F., Skvoretz, J., 2010. Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks 32 (3), 245-251

  23. Enabled Analytics:The vision • Resources • The igraph library http://igraph.sourceforge.net/ • www.orgnet.com • S.N.A.P. http://snap.stanford.edu/snap/index.html

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