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  • 5.1.4 Studying Four Major NetSci Researchers (ISI Data)

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  1. Resize Linear > Nodes > globalcitationcount> From: 1 To: 50 > When the nodes have no 'globalcitationcount': 0.1 > Do Resize Linear
  2. Colorize > Nodes > globalcitationcount > From:   To:   > When the nodes have no 'globalcitationcount': 0.1 >   >Do Colorize
  3. Colorize > Edges > weight > From (select the "RGB" tab) 127, 193, 65 To: (select the "RGB" tab) 0, 0, 0
  4. Type in Interpreter:
Code Block

>for n in g.nodes:
     n.strokecolor = n.color

Or, select the 'Interpreter' tab at the bottom, left-hand corner of the GUESS window, and enter the command lines:

Code Block

> resizeLinear(globalcitationcount,1,50)
> colorize(globalcitationcount,gray,black)
> for e in g.edges:
      e.color="127,193,65,255"

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The 'FourNetSciResearchers' dataset has exactly 65 isolates. Removing those leaves 12 networks shown in Figure 5.11 (right) using the same color and size coding as in Figure 5.11 (left). Using 'View > Information Window' in GUESS reveals detailed information for any node or edge.
Alternatively, nodes could have been color and/or size coded by their degree using, e.g.:

Code Block

> g.computeDegrees()
> colorize(outdegree,gray,black)

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The largest component has 2407 163 nodes; the second largest, 30745; the third, 1324; and the fourth has 7 12 nodes. The largest component is shown in Figure 5.12. The top 20 papers, by times cited in ISI, have been labeled using

Code Block

> toptc = g.nodes[:]
> def bytc(n1, n2):
      return cmp(n1.globalcitationcount, n2.globalcitationcount)
> toptc.sort(bytc)
> toptc.reverse()
> toptc
> for i in range(0, 20):
      toptc[i].labelvisible = true

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The updated co-authorship network can be visualized using 'Visualization > Networks > GUESS', (See section 4.9.4.1 GUESS Visualizations for more information regarding GUESS).
Figure 5.13 shows the layout of the combined 'FourNetSciResearchers' dataset after it was modified using the following commands in the "Interpreter":   

Code Block

> resizeLinear(numberofworks,1,50)
> colorize(numberofworks,gray,black)
> for n in g.nodes:
      n.strokecolor = n.color
> resizeLinear(numberofcoauthoredworks, .25, 8)
> colorize(numberofcoauthoredworks, "127,193,65,255", black)
> nodesbynumworks = g.nodes[:]
> def bynumworks(n1, n2):
     return cmp(n1.numberofworks, n2.numberofworks)
> nodesbynumworks.sort(bynumworks)
> nodesbynumworks.reverse()
> for i in range(0, 50):
      nodesbynumworks[i].labelvisible = true

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This network can be visualized in GUESS; see Figure 5.14. Nodes and edges can be color and size coded, and the top 20 most-cited papers can be labeled by entering the following lines in the GUESS "Interpreter":

Code Block

> resizeLinear(globalcitationcount,2,40)
> colorize(globalcitationcount,(200,200,200),(0,0,0))
> resizeLinear(weight,.25,8)
> colorize(weight, "127,193,65,255", black)
> for n in g.nodes:
      n.strokecolor=n.color
> toptc = g.nodes[:]
> def bytc(n1, n2):
      return cmp(n1.globalcitationcount, n2.globalcitationcount)
> toptc.sort(bytc)
> toptc.reverse()
> toptc
> for i in range(0, 20):
      toptc[i].labelvisible = true

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Once edges have been removed, the network "top 1000 edges by weight" can be visualized by running 'Visualization > Networks > GUESS'. In GUESS, run the following commands in the Interpreter:

Code Block

> for node in g.nodes:
      node.x = node.xpos * 40
      node.y = node.ypos * 40
> resizeLinear(references, 2, 40)
> colorize(references,[200,200,200],[0,0,0])
> resizeLinear(weight, .1, 2)
> g.edges.color = "127,193,65,255"

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The largest speed increases from the database functionality can be found in the extraction of networks. First, compare the results of a co-authorship extraction with those from section 5.1.4.2 Author Co-Occurrence (Co-Author) Network. Run 'Data Preparation > Database > ISI > Extract Co-Author Network' followed by 'Analysis > Networks > Network Analysis Toolkit (NAT)'. Notice that both networks have 247 nodes and 891 edges. Visualize the extracted co-author network in GUESS using 'Visualization > Networks > GUESS' and reformat the visualization using 'Layout > GEM' and 'Layout > Bin Pack.' To apply the default co-authorship theme, go to 'Script > Run Script' and find 'yoursci2directory/scripts/GUESS/co-author-nw_database.py'. The resulting network will look like Figure 5.21.

Figure 5.21: Longitudinal study of 'FourNetSciResearchers,' visualized in GUESS

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