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  • 5.1.4 Studying Four Major NetSci Researchers (ISI Data)

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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()

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> colorize(outdegree,gray,black)

Note that the outdegree corresponds to the LCC within the given network while the indegree reflects the number of references, helping to visually identify review papers.

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The largest component has 2407 nodes; the second largest, 307; the third, 13; and the fourth has 7 nodes. The largest component is shown in Figure 5.12. The top 20 papers, by times cited in ISI, have been labeled using Wiki Markup     

Code Block

> toptc = g.nodes

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[:

...

]

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> def bytc(n1, n2):

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      return cmp(n1.globalcitationcount, n2.globalcitationcount)

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> toptc.sort(bytc)

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> toptc.reverse()

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> toptc
> for i in range(0, 20):

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      toptc[i].labelvisible = true

Alternatively, run 'Script > Run Script' and select 'yoursci2directory/scripts/GUESS/paper-citation-nw.py'.

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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)

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> colorize(numberofworks,gray,black)

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> for n in g.nodes:

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      n.strokecolor = n.color

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> resizeLinear(numberofcoauthoredworks, .25, 8)

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> colorize(numberofcoauthoredworks, "127,193,65,255", black)

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> nodesbynumworks = g.nodes

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[:

...

]

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> def bynumworks(n1, n2):

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     return cmp(n1.numberofworks, n2.numberofworks)

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> nodesbynumworks.sort(bynumworks)

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> nodesbynumworks.reverse()

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> for i in range(0, 50):

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      nodesbynumworks[i].labelvisible = true

Alternatively, run 'Script > Run Script ...' and select 'yoursci2directory/scripts/GUESS/co-author-nw.py'.

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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)

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> colorize(globalcitationcount,(200,200,200),(0,0,0))

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> resizeLinear(weight,.25,8)

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> colorize(weight, "127,193,65,255", black)

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> for n in g.nodes:

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      n.strokecolor=n.color

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> toptc = g.nodes

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[:

...

]

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> def bytc(n1, n2):

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      return cmp(n1.globalcitationcount, n2.globalcitationcount)

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> toptc.sort(bytc)

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> toptc.reverse()

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> toptc
> for i in range(0, 20):

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      toptc[i].labelvisible = true

Alternatively, run 'GUESS: File > Run Script ...' and select 'yoursci2directory/scripts/GUESS/reference-co-occurence-nw.py'.

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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:

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      node.x = node.xpos * 40

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      node.y = node.ypos * 40

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Wiki Markup
     \[tab\]

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> resizeLinear(references, 2, 40)

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> colorize(references,

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[200,200,200

...

],

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[0,0,0

...

])

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> resizeLinear(weight, .1, 2)

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> g.edges.color = "127,193,65,255"

The result should look something like Figure 5.16.

Figure 5.16: Undirected, weighted word co-occurrence network visualization for the DrL-processed 'FourNetSciResearchers' dataset

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