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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