# edge weights vs multi-graphs in minimize_blockmodel_dl Classic List Threaded 5 messages Open this post in threaded view
|

## edge weights vs multi-graphs in minimize_blockmodel_dl

 How does minimize_blockmodel_dl deal with multi-graphs (graphs where multiple edges can exist between two vertices)? Is the information discarded, or is it incorporated as edge weight, and if so, how? Sorry if this is addressed in the documentation. -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/_______________________________________________ graph-tool mailing list [hidden email] https://lists.skewed.de/mailman/listinfo/graph-tool
Open this post in threaded view
|

## Re: edge weights vs multi-graphs in minimize_blockmodel_dl

 Administrator Am 21.05.20 um 22:25 schrieb sam: > How does minimize_blockmodel_dl deal with multi-graphs (graphs where multiple > edges can exist between two vertices)? > > Is the information discarded, or is it incorporated as edge weight, and if > so, how? If there are parallel edges in the graph, they are taken into account, since the underlying SBM accepts multigraphs. This is done automatically, and there is nothing special that needs to be done. -- Tiago de Paula Peixoto <[hidden email]> _______________________________________________ graph-tool mailing list [hidden email] https://lists.skewed.de/mailman/listinfo/graph-tool signature.asc (849 bytes) Download Attachment -- Tiago de Paula Peixoto
Open this post in threaded view
|

## Re: edge weights vs multi-graphs in minimize_blockmodel_dl

 Thanks. Could you clarify the difference between (1) loading a graph with parallel edges g and running minimize_blockmodel_dl(g) on it (2) loading a graph with simple edges but an edge property equal to the number of parallel edges and running: state_args=dict(recs=[g.ep.weight], rec_types=["discrete-poisson"])) Sorry if this question is obvious; I've read your "Nonparametric weighted stochastic block models", "Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models", and "Nonparametric Bayesian inference of the microcanonical stochastic block model" papers. I am just not 100% clear on the mapping from the papers to what the function does, in this case. I am getting some quite different results depending on whether I use method (1) or (2), but that may be due to randomness. -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/_______________________________________________ graph-tool mailing list [hidden email] https://lists.skewed.de/mailman/listinfo/graph-tool
 Administrator Am 22.05.20 um 07:33 schrieb sam: > Thanks. > > Could you clarify the difference between > > (1) loading a graph with parallel edges g and running > minimize_blockmodel_dl(g) on it > > (2) loading a graph with simple edges but an edge property equal to the > number of parallel edges and running: > state_args=dict(recs=[g.ep.weight], rec_types=["discrete-poisson"])) The first is the regular degree-corrected Poisson SBM. The second is a version with edge covariates, where we first sample from the Poisson SBM, and then we sample weights on edges according to a specific distribution (in your case, again a Poisson). This is not equivalent to sampling from a single Poisson, since the 'weight' Poisson covariates are sampled only on the non-zero entries of the first Poisson. So, for example, we may have a sparse graph, with most pairs of nodes not being connected, but where every edge has an integer covariate that is very high, say on the order of 100, which would not be possible to generate with model 1 without the whole graph becoming dense. Best, Tiago -- Tiago de Paula Peixoto <[hidden email]> _______________________________________________ graph-tool mailing list [hidden email] https://lists.skewed.de/mailman/listinfo/graph-tool signature.asc (849 bytes) Download Attachment -- Tiago de Paula Peixoto