[graph-tool] Re: random graph
Tiago de Paula Peixoto wrote: > Am 01.09.21 um 19:24 schrieb Davide Cittaro: > > > If we assume that the big graph is sampled from a SBM, then the > sub-sampled graph would also be sampled from a SBM, but not from the > same one, if we are dealing with sparse networks. The sub-sampled SBM > would be sparser (smaller average degree), and have a deformed degree > distribution in the case of the DC-SBM. Since my graphs are kNN graphs, would you suggest to recompute them on subsampled data? To be fair I’ve tried and results do not change dramatically > > The intuition here is that the evidence for the underlying structure > will become weaker after sub-sampling, according to how sparser the > network becomes. With the MDL/Bayesian approach in graph-tool, you > should see fewer groups in the sub-sampled network, but they should > otherwise be similar to the full network. This is indeed what I observe. I’m thinking to use this possibility to “sniff” the data and, in case needed, one can (and should) use the full network. Also, I’m aware subsampling will generally wipe out small communities which won’t be identified Thanks again d ___ graph-tool mailing list -- graph-tool@skewed.de To unsubscribe send an email to graph-tool-le...@skewed.de
[graph-tool] Re: random graph
Am 01.09.21 um 19:24 schrieb Davide Cittaro: Following up this post, I have large datasets for which I already have a NSBM. I wanted to speed up thinking to an approximate model, so I subsampled a fraction of nodes (randomly chosen) and performed NSBM, then performed a sort of label transfer to the original graph. Except for the fact the partitions at level 0 are now larger than the original ones (as expected) I noticed a general concordance between the communities using subsampled and full graphs. Do you have some literature, ideas or hints about analysis of subsamples? If we assume that the big graph is sampled from a SBM, then the sub-sampled graph would also be sampled from a SBM, but not from the same one, if we are dealing with sparse networks. The sub-sampled SBM would be sparser (smaller average degree), and have a deformed degree distribution in the case of the DC-SBM. The intuition here is that the evidence for the underlying structure will become weaker after sub-sampling, according to how sparser the network becomes. With the MDL/Bayesian approach in graph-tool, you should see fewer groups in the sub-sampled network, but they should otherwise be similar to the full network. -- Tiago de Paula Peixoto ___ graph-tool mailing list -- graph-tool@skewed.de To unsubscribe send an email to graph-tool-le...@skewed.de
[graph-tool] Re: random graph
Following up this post, I have large datasets for which I already have a NSBM. I wanted to speed up thinking to an approximate model, so I subsampled a fraction of nodes (randomly chosen) and performed NSBM, then performed a sort of label transfer to the original graph. Except for the fact the partitions at level 0 are now larger than the original ones (as expected) I noticed a general concordance between the communities using subsampled and full graphs. Do you have some literature, ideas or hints about analysis of subsamples? ___ graph-tool mailing list -- graph-tool@skewed.de To unsubscribe send an email to graph-tool-le...@skewed.de
