Hi Tiago, Apologies for not being clearer. Let me try and make my example more specific:
I have a network defined from brain imaging and am passing an edge weight of a given clinical variable. In this example the nodes are voxels of brain tissue, the edges are the presence of the voxels being structurally connected in imaging space, and the edge weight is the relationship of this to a clinical variable, a weight which incorporates ageing. I want to firstly derive the community structure, passing the edge weight, which ultimately gives me clusters of voxels. But, in addition I want to derive some formulation of a weight for the community blocks for their relation to the passed edge weight. For instance, in this example I would want a block which contains voxels within the hippocampus to be negatively associated to an age weight given atrophy associated with age, but a block containing voxels of the ventricular system to be positively associated as they will enlarge with age. How would you go about doing this? BW James On 23 Nov 2021, at 14:53, Tiago de Paula Peixoto <[email protected]<mailto:[email protected]>> wrote: Am 23.11.21 um 15:46 schrieb James Ruffle: This yields a hierarchical community structure, but how would you most suitably determine what communities were ‘most’ or ‘least’ important/influential/correlated with respect to the edge weight? I have considered whether this might be done with centrality metrics on the blocks (or perhaps vcount and ecount data from a condensation graph on the hierarchical blocks), but was keen to see if you had a more innovative idea... It is impossible to answer this kind of question absent of a very specific context and objective in mind. One of the biggest sins in network science is the proliferation of centrality metrics that attempt to define which node is "best" or "most important" as if there was a general answer to this question. So, I can't tell you which community is most "important"; you have to tell me what you mean by this. Best, Tiago -- Tiago de Paula Peixoto <[email protected]<mailto:[email protected]>> _______________________________________________ graph-tool mailing list -- [email protected]<mailto:[email protected]> To unsubscribe send an email to [email protected]<mailto:[email protected]>
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