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  • 5.1.4 Studying Four Major NetSci Researchers (ISI Data)

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Given the co-Authorship network extracted at workflow 5.1.4.2, run 'Preprocessing > Networks > Fast Pathfinder Network Scaling' with the following parameters:

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Visualize this network using 'Visualization > Networks > GUESS'. Inside GUESS, run 'Script > Run Script ...' and select ' yoursci2directory/scripts/GUESS/co-author-nw.py' again. Also run 'Layout > GEM' and then 'Layout > Bin Pack' to give a better representation of node clustering. The pruned network with the algorithm  looks like this:

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Degree Distribution

Given the co-Authorship network extracted at workflow 5.1.4.2 (name 'Updated Network' at the Data Manager), run 'Analysis > Networks > Unweighted & Undirected > Degree Distribution' with the following parameter:

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This algorithm generates two output files, corresponding to two different ways of partitioning the interval spanned by the values of degree.

In the first output file named 'Distribution of degree for network at study (equal bins)', the occurrence of any degree value between the minimum and the maximum is estimated and divided by the number of nodes of the network, so to obtain the probability: the output displays all degree values in the interval with their probabilities. Visualize this first output file with 'Visualization > General > GnuPlot':

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The second output file named 'Distribution of degree for network at study (logarithmic bins)' gives the binned distribution, i.e. the interval spanned by the values of degree is divided into bins whose size grows while going to higher values of the variable. The size of each bin is obtained by multiplying by a fixed number the size of the previous bin. The program calculates the fraction of nodes whose degree falls within each bin. Because of the different sizes of the bins, these fractions must be divided by the respective bin size, to have meaningful averages.

This second type of output file is particularly suitable to study skewed distributions: the fact that the size of the bins grows large for large degree values compensates for the fact that not many nodes have high degree values, so it suppresses the fluctuations that one would observe by using bins of equal size. On a double logarithmic scale, which is very useful to determine the possible power law behavior of the distribution, the points of the latter will appear equally spaced on the x-axis.

Visualize also this second output file with 'Visualization > General > GnuPlot':

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5.1.4.3 Cited Reference Co-Occurrence (Bibliographic Coupling) Network

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