graph-tool April 2022

graph-tool@skewed.de

3 participants
2 discussions

A question about generating simple graphs in graph-tool
by Snehal Shekatkar 04 May '22

04 May '22

Dear Tiago, I happened to be looking at the code for random graph generation (graph_generation.hh). The code checks whether a generated degree sequence is Graphic or not using Erdos-Gallai condition. If the condition is not satisfied, the code seems to randomly choose a vertex and re-generate its degree. This is logical in most cases. However, if any of the degree value is greater than "n-1", the size of the graph, clearly a simple graph cannot be generated from such sequence, and it should be immediately changed by re-generating degrees for such vertices. This happens a lot when the degrees are sampled from a right-skewed distribution. But I don't see anything like this in the code. My question is, isn't it inefficient to keep randomly choosing vertices one at a time and change their degrees until the vertices with degree greater than n-1 are reassigned degrees? Is it being done for correctly sampling from a given degree distribution? Or am I missing something? Thank you. Best regards, SMS -- Snehal M. Shekatkar https://inferred.in PGP key -> https://inferred.in/snehal_pub_key.txt

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Inference for weighted, hierarchical stochastic block models
by Alexander Barron 12 Apr '22

12 Apr '22

Hello Tiago and community, I have a network that I'd like to infer a hierarchical SBM from, but it has weighted edges. After reading the weighted SBM paper [1] and looking at the graph-tool docs, I think I might be able to use `*minimize_nested_blockmodel_dl*` and pass the `*BlockState*` `*eweight*` argument via `*state_args*`. The weights are discrete and non-negative, so maybe I also need to specify " discrete-geometric" via `*rec_types`*. The edge weights could also be seen as multiple edges in a multigraph. 1]: https://arxiv.org/pdf/1708.01432.pdf After searching through old mailing list posts, here is my current attempt for the weighted case: ``` g = Graph() # Add vertices here # Add edges here edge_weights = g.new_edge_property('int') # Specify edge weights here state = minimize_nested_blockmodel_dl(g, state_args=dict(eweight=edge_weights, rec_types=['discrete-geometric']) ) ``` Am I on the right track? And would it be better to specify the edge weights via the `*BlockState*` `*recs*` parameter instead of using ` *eweight*`? Thank you, Alexander Alexander T. J. Barron https://cogentmentat.github.io/academic/

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graph-tool April 2022