[graph-tool] Re: Modelling with community structure data

[Tiago de Paula Peixoto]

Dear James,

Am 23.11.21 um 16:07 schrieb James Ruffle:

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?


The seemingly obvious answer is to look at the distribution of edge 
covariates on edges incident on the groups. But it is still not very 
clear exactly what you want to find.


In any case, this is a question about a particular research problem, so 
I don't believe it is appropriate for this list, which is about using 
graph-tool.


Best,
Tiago

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Tiago de Paula Peixoto 
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[graph-tool] Re: Modelling with community structure data

[Lietz, Haiko]

Hi James,

I’m wondering why you use the Stochastic Blockmodel if you want to identify 
community structure? The SBM groups nodes with similar positions but, roughly 
speaking, does not maximize the number of edges that constitute communities.

Best

Haiko


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[graph-tool] Re: Modelling with community structure data

[James Ruffle]

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 
mailto:ti...@skewed.de>> 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

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Tiago de Paula Peixoto mailto:ti...@skewed.de>>
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[graph-tool] Re: Modelling with community structure data

[Tiago de Paula Peixoto]

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 
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[graph-tool] Modelling with community structure data

[James Ruffle]

Dear Tiago / community,

We are using the stochastic block model in a research project and trying to 
formulate how we would best utilise the community structure results downstream, 
and would welcome any suggestions.

We fit a nested stochastic block model with an edge weight, which gives us a 
hierarchical partition. From this, we want to report not just the underlying 
community structure, but also some form of corresponding weights of given 
blocks, how ‘important' a given block is with respect to the edge weight, say.

Considering an example on the weighted foodweb network:

import graph_tool.all as gt; import numpy as np; import matplotlib;
g = gt.collection.ns["foodweb_baywet"]
state = gt.minimize_nested_blockmodel_dl(g, state_args=dict(recs=[g.ep.weight],

rec_types=["real-exponential"]))

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...

Thank you for your advice!
James
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