Ok, thanks! I'll run the algorithm more times then to try to find the best fit.
Best regards, Andrea 2016-06-08 10:14 GMT+02:00 Andrea Briega <[email protected]>: > Thank you very much, your answers have been really helpful. I am now on > the last step, model selection, and I would like to be sure that I’m doing > it right. I get the posterior odds ratio to compare two partitions throught > this way: e^-(dl1-dl2), with dl1 and dl2 as higher and lower description > length respectively. I have obtained description length using > ‘state.entropy()’ for nested models and ‘state.entropy(dl=True)’ for no > nested ones. > I have doubts about this because small differences in description length > cause much lower values than 0.01, so in most cases the evidence supporting > one of the models is decisive. I only get higher values than 0.01 if the > difference in description length is lower than 5 units. With my data > (24.000 nodes and 5.000.000 edges) I always obtain decisive supports, > either when I compare different models or when I compare different runs of > the same model. I wonder if this is rigth. > > Thanks again, > > > Andrea > > 2016-05-19 9:55 GMT+02:00 Andrea Briega <[email protected]>: > >> 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 >>>>>> >>>>> >>>>> >>>> >>> >> >
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