WOLFRAM NOTEBOOK

Examples in Multiparadigm Data Science:
GlobalClusteringCoefficient

A simulation of information diffusion in a social network using density heat maps
to analyze connectivity and nearness in a small-world network.

GlobalClusteringCoefficient efficiently calculates the connectivity of undirected graphs, directed graphs
and multigraphs from SocialMediaData, using ColorData and Grid for easy visual interpretation of the results.

Define a function to compute the GlobalClusteringCoefficient of a set of random graphs:

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{graphs,coeffs}=Transpose[SortBy[{#,GlobalClusteringCoefficient[#]}&/@RandomGraph[WattsStrogatzGraphDistribution[200,0.1,3],{10000}],Last]];

Create a heat map of selected graphs with respect to the coefficient values using ColorData and Grid:

In[2]:=

min=Min[coeffs];max=Max[coeffs];nf=Nearest[coeffsgraphs];values=Range[min,max,(max-min)/(4^2-1)];Grid[Partition[Map[SetProperty[First[nf[#]]1,{VertexStyleDirective[White,EdgeForm[White]],EdgeStyleWhite,BackgroundColorData[{"SolarColors","Reversed"},Rescale[#,{min,max}]],ImageSize{100,100}}]&,values],4]]

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