GraphCrunch
A Tool for Large Network Analyses
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An example of the tabular output file
An example of the output file resulting from processing the real-world network Net1.gw by GraphCrunch. The five network models and all of the currently supported properties are presented. Three networks per random graph model were generated (denoted by 1, 2, and 3 in column “Random Networks/Stats”). Thus, the total number of random networks analyzed in this example is 3x5=15. The column denoted by “Data Network” contains the name of the real-world network being analyzed. The column denoted by “Model Networks” contains the names of network models against which the data is being compared (“er”, “er dd”, “sf”, “geo”, and “sticky”). Random graphs from the same network model are denoted by a sequence of integers presented in column “Random Network/Stats”; in the same column, “AVG” and “STDDEV” denote that the fields in these rows contain averages and standard deviations of network properties (given in columns to the right) computed over all random graphs from the given network model that were generated and analyzed by GraphCrunch. The columns denoted by “Average Diameter” and “Clustering Coefficient” contain the average diameter and the average clustering coefficient of a network, respectively. The column denoted by “Total Number of Graphlets” contains the total number of all 2-5-node graphlets in a network. The columns denoted by “Degree Distribution (Pearson)”, “Distance Spectrum (Pearson)” and “Clustering Spectrum (Pearson)” contain the Pearson’s rank correlation coefficients of degree distributions, distance spectra and clustering spectra between the real-world and model networks, respectively. The columns denoted by “GDD agreement (amean)” and “GDD agreement (gmean)” contain the arithmetic and geometric means of GDD-agreements between the real-world and model networks, respectively. Finally, the column denoted by “RGF Distance” contains RGF-distances between the data and model networks.
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