[graph-tool] Questions about inference algorithms

Andrea Briega annbrial at gmail.com
Thu May 12 10:56:43 CEST 2016


Hi again,

I have recently noticed the actualization of graphtool and now I am a
little bit confused about some changes. Sorry, I know my questions are very
basic. I am not familiar with these language and I have some dificulties to
get results.

I am running inference algorithms to get the best model using different
options of model selection. I want to set pclabel in the inference
algorithms because I know a priori my network is bipartite, and next I want
to get the description length. Before actualization I did this by this way:

vprop_double = g.new_vertex_property("int") # g is my network
 for i in range(0, 11772):
     vprop_double[g.vertex(i)] = 1
 for i in range(11773, 214221):
     vprop_double[g.vertex(i)] = 2

state = gt.minimize_blockmodel_dl(g, pclabel=True)

state.entropy(dl=True) # I am not sure this is the right way to get the
description length.

But now I have some problems. First of all, minimize_blockmodel_dl doesn't
have a pclabel argument so I don't know how indicate it in the inference
algorithm. I have tried this:

state.pclabel = vprop_double

But I get the same result when I do "state.entropy(dl=True)" as before.
Also, I get the same result doing "state.entropy(dl=True)" or
"state.entropy()", and I don't understand why neither.

And finally, in NestedBlockState objects I don't know to get description
length because entropy hasn't a "dl" argument. In these objects entropy and
dl are the same?

In conclusion, I don't know how to set pclabel and to get the description
length in hierarchical models, and I am not sure if I am getting it
correctly in non-hierarchical ones.

Sorry again for my basic questions but I can't go on because of these
problems.

Thank you very much!

Best regards,




Andrea




2016-05-10 11:41 GMT+02:00 Andrea Briega <annbrial at gmail.com>:

> Thank you very much! your answer has been really helpful, now I understand
> this much better. I'll think about the options you said.
>
> Thanks again,
>
>
> Andrea
>
> 2016-05-09 16:33 GMT+02:00 Andrea Briega <annbrial at gmail.com>:
>
>> Dear Dr Peixoto,
>>
>>
>> I would like to solve some questions I have about inference algorithms
>> for the identification of large-scale network structure via the statistical
>> inference of generative models.
>>
>> Minimize_blockmodel algorithm takes an hour to finish using my network
>> with 21000 nodes (like the hierarchical version), and it spends two days
>> and a half with overlap. However, I have run an hierarchical analysis with
>> overlap, and it is still running since 14 days ago. So my first question
>> is: is this time normal, or maybe there is any problem? Do you know how
>> long could it ussually takes?
>>
>> Secondly, I have repeated some of these analysis with exactly same
>> options but I get different solutions (similar but different), so I wonder
>> if the algorithm is heuristic (I thought it was exact).
>>
>> My last question question regards bipartite analysis. I have two types of
>> nodes in my network and I wonder if there are any analytical difference
>> when running these algorithms with the bipartite option (clabel=True, and
>> different labels in each group of nodes) or not, because it seems that the
>> program “knows” my network is bipartite in any case. If there are
>> differences between bipartite and “unipartite” analysis (clabel=False), is
>> it possible to compare description length between them to model selection?
>>
>> Thank you very much for your help!
>>
>>
>> Best regards,
>>
>>
>>
>> Andrea
>>
>
>
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