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R, IGRAPH, AND
HANDSOME GRAPHS

       &
OVERVIEW
HISTORY OF PHILOSOPHY
•


•




•
GET THE DATA: THE QUERY
•                # Create the query
                 qq <- 'SELECT * WHERE {
    –
                 ?p a
                 <http://dbpedia.org/onto
                 logy/Philosopher> . ?p
                 <http://dbpedia.org/onto
                 logy/influenced>
    –            ?influenced. }'
    –
                 # Use it in SPARQL
•                data <-
                 SPARQL(url='http://dbped
                 ia.org/sparql',query=qq)
GET THE DATA: CLEANING
•                # Prep to make it directed
                 orig <- unlist(data$results[[2]],
                 use.names=F)
                 dest <- unlist(data$results[[1]],
         &       use.names=F)

                 # Turn URLs into readable names
•                orig <- url2names(orig)
    –            dest <- url2names(dest)

    –            # Format as an edge graph.
                 edges <-
                 data.frame(cbind(as.matrix(orig),as.
                 matrix(dest), rep(1,length(orig))),
                 stringsAsFactors=F)

                 # TADA!
R TOOLS
•
    –


    –

    –
USING IGRAPH
•                             g <- graph.data.frame(edges, direct=TRUE,
                              vertices=NULL)
    –
    –                         SNAbasics <- function(X) {
    –                         V(X)$label <- V(X)$name
                              comps <- leading.eigenvector.community(X,
    –                         options=list(naive=F))$membership
•                             colbar <- rainbow(max(comps)+1)
                              V(X)$color <- colbar[comps+1]
    –                         # REMOVE OPACITY
        • V(g) <- values      V(X)$color <- substr(V(X)$color,1,7)
        • set.vertex.attrib   RPageRank <- page.rank(X, vids=V(X),
          ute(g,values)       directed=TRUE,
    –                         options=list(maxiter=10000,eps=0.0001))$ve
                              ctor
                              V(X)$RPageRank <- RPageRank*1000
                              V(X)$size <- RPageRank*1000
    –                         V(X)$Katz <- evcent(X)$vector*1000
                              print(length(unique(comps)))
                              return(X)
                              }
VISUALIZATION LAYOUTS
R VISUALIZATION OPTIONS
R2OTHER
•
    –
•
    –
•
    –
•
    –
    –       python -m SimpleHTTPServer 8000
        •
        •
GRAPH DATABASES
•
    –

    –
•
    –

    –


    –
TO GEPHI
•
•
    –



        • write.graph(g,
          file='gephi.GML',
          format='GML')

    –
TO GRAPHVIZ (WITH DOT)
•
•


•


•
JSON -> D3
•
•
    –
    –
    –

•
    – As.numeric
    – stringsAsFactors
      =FALSE
    –
GEPHI -> SIGMA.JS
•
•




•
HAIRBALLS AND HIVES
•




•
INTERACTIVE HAIRBALLS?
•



•


    –


•
•
CHALLENGES!
•                                  graph2web()
    –
    –
        • PlotInMyBrowser(mydata,myport)
    –
•
    –
•
THANKS
•
•
•
•

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Rug hogan-10-03-2012

  • 4. GET THE DATA: THE QUERY • # Create the query qq <- 'SELECT * WHERE { – ?p a <http://dbpedia.org/onto logy/Philosopher> . ?p <http://dbpedia.org/onto logy/influenced> – ?influenced. }' – # Use it in SPARQL • data <- SPARQL(url='http://dbped ia.org/sparql',query=qq)
  • 5. GET THE DATA: CLEANING • # Prep to make it directed orig <- unlist(data$results[[2]], use.names=F) dest <- unlist(data$results[[1]], & use.names=F) # Turn URLs into readable names • orig <- url2names(orig) – dest <- url2names(dest) – # Format as an edge graph. edges <- data.frame(cbind(as.matrix(orig),as. matrix(dest), rep(1,length(orig))), stringsAsFactors=F) # TADA!
  • 6. R TOOLS • – – –
  • 7. USING IGRAPH • g <- graph.data.frame(edges, direct=TRUE, vertices=NULL) – – SNAbasics <- function(X) { – V(X)$label <- V(X)$name comps <- leading.eigenvector.community(X, – options=list(naive=F))$membership • colbar <- rainbow(max(comps)+1) V(X)$color <- colbar[comps+1] – # REMOVE OPACITY • V(g) <- values V(X)$color <- substr(V(X)$color,1,7) • set.vertex.attrib RPageRank <- page.rank(X, vids=V(X), ute(g,values) directed=TRUE, – options=list(maxiter=10000,eps=0.0001))$ve ctor V(X)$RPageRank <- RPageRank*1000 V(X)$size <- RPageRank*1000 – V(X)$Katz <- evcent(X)$vector*1000 print(length(unique(comps))) return(X) }
  • 10. R2OTHER • – • – • – • – – python -m SimpleHTTPServer 8000 • •
  • 11. GRAPH DATABASES • – – • – – –
  • 12. TO GEPHI • • – • write.graph(g, file='gephi.GML', format='GML') –
  • 13. TO GRAPHVIZ (WITH DOT) • • • •
  • 14. JSON -> D3 • • – – – • – As.numeric – stringsAsFactors =FALSE –
  • 18. CHALLENGES! • graph2web() – – • PlotInMyBrowser(mydata,myport) – • – •
  • 19.