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 >>> >> >> >
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