[graph-tool] Questions about inference algorithms

Andrea Briega annbrial at gmail.com
Thu May 12 18:43:55 CEST 2016


Thank you very much! I was wrong, I meant "state =
gt.minimize_blockmodel_dl(g, pclabel=vprop_double)", it has been a mistake
while I was writing the mail. So the key was the use of "state_args", I
tried it but with a different notation and obviously it didn't work. Now I
can go on!

Thanks again,


Andrea


2016-05-12 10:56 GMT+02:00 Andrea Briega <annbrial at gmail.com>:

> 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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