On 24.03.2017 11:18, yerali wrote: > Dear Tiago, > Thanks for your answer. > Sorry for the delay of my reply but I was learning how to add a property > from external data. > > I just wanted to answer: >> And how do you make this assessment? > > So, in this picture the separation into communities using partition > stability, as an example: > <http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/file/n4027150/dib_ps.jpg> > > I think colors well represent set of nodes densely connected, which is > important at least in my current case of study, where links represent > causality.
This is a perfect example of what I am trying to convey to you. What you are seeing are basically three groups of nodes with low degree around nodes with high in-degree, in addition to some smaller groups. Any random graph with the same degree sequence will admit similar partitions, although they carry no meaning from a generative point of view. You can check this by randomizing your graph with random_rewire() and then obtaining the partition again with the same method. You will probably see a similar division. In other words, these are not statistically significant communities; they arise out of random fluctuations. Best, Tiago -- Tiago de Paula Peixoto <[email protected]>
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