So the next step was to assign attributes 'color' and 'trust' to each node. The 'color' attribute ranges from 1 to 50 , while 'trust' ranges between 0 and 1.
And for n-iterations, randomly pick a each edge, and based on the difference between their 'belief' attributes, the 'color' attribute of one vertex node influences the attribute of the other vertex.
The result on 10,000 nodes, wit 4 average connections each, over 100,00 iteration are shown below.
The first figure is for model 1, where left side sows the initial clustering, and right shows the final clusters.
And for model 2, it looks like
and
And for n-iterations, randomly pick a each edge, and based on the difference between their 'belief' attributes, the 'color' attribute of one vertex node influences the attribute of the other vertex.
The result on 10,000 nodes, wit 4 average connections each, over 100,00 iteration are shown below.
The first figure is for model 1, where left side sows the initial clustering, and right shows the final clusters.
And for model 2, it looks like
The same graphs, if the "trust" property is not used, would respectively look as
and
Next, I am working on more simulations, and generalising the "trust" attribute to be a composite of different attributes.
So Long.
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