Dear Mr Peixoto,
I have just run a few analysis of the new version of your package and my results totally change between v2.13 and v2.16. Nested_minimize_blockmodel is the one that make most relevant changes and it is very difficult to get a biological explanation of the new results, mainly at the superior hierarchical levels. I would like to know the particular changes in these two analysis to better understanding of my results. Is it possible to change any parameter to run this function in a similar way to the v2.13? I used to run this function on this way: state = minimize_nested_blockmodel_dl(g, pclabel=vprop_double, overlap=False, nonoverlap_init=False, deg_corr=True, layers=False) And I have run the new version of the function on this way: state = minimize_nested_blockmodel_dl(g, state_args=dict(pclabel=vprop_double), overlap=False, nonoverlap_init=False, deg_corr=True, layers=False) Thank you very much, Andrea 2016-05-12 18:43 GMT+02:00 Andrea Briega <[email protected]>: > 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 <[email protected]>: > >> 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 <[email protected]>: >> >>> 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 <[email protected]>: >>> >>>> 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 >>>> >>> >>> >> >
_______________________________________________ graph-tool mailing list [email protected] https://lists.skewed.de/mailman/listinfo/graph-tool
