January 2020 - graph-tool - archives.skewed.de

Dynamics and the SIRSState Package
by James Ruffle 16 Jan '20

16 Jan '20

Dear Tiago/Community, I am interested in using the SIS (and SIRS) models to simulate dynamic processes. However, rather than pass a singular transmission probability, per the documentation, I am interested in using edge property maps as weights for the beta value - a weighted SIS. Is this possible in graph tool? (It sounds like this is feasible with EoN, https://epidemicsonnetworks.readthedocs.io/en/latest/examples/SIS_weighted.… , but where possible I would rather stick to GraphTool…!) James

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projecting vertex marginals to hierarchy levels
by Davide Cittaro 16 Jan '20

16 Jan '20

Hi again, I'm following the cookbook and I'm collecting vertex marginals during mcmc_equilibrate, so pv = [None] * len(state.get_levels()) def collect_marginals(s): global pv pv = [sl.collect_vertex_marginals(pv[l]) for l, sl in enumerate(s.get_levels())] gt.mcmc_equilibrate(state, force_niter=1000, mcmc_args=dict(niter=10), wait=wait, nbreaks=nbreaks, epsilon=epsilon, callback=collect_marginals ) After this is done, every element of pv will be, basically, a list of arrays with as many elements as many vertexs are at specific level, each with the counts for memberships. In fact, when I need to assign groups to the original vertexs to a blockstate at level L > 0 (say, level 2) I can use state.project_partition(2, 0) the vertex map will contain as many elements as the number of vertex of my graph, each labeled according to the blocks at level 2 of the hierarchy. I cannot find a smart way to project the marginals I've collected, would it be sufficient to sum numbers for pv[0] following the hierarchy (that is, summing counts for all groups at level 0 which are included in the same group at level 2)? Thanks d

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graph-tool and GPU offloading
by Davide Cittaro 16 Jan '20

16 Jan '20

Hi again, A quick question: does graph-tool support GPU computation seamlessly? Our institute is pushing on that kind of hardware, I want to understand if I can take advantage of it. Best, d

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Nested Block State hierarchy level after mcmc_equilibrate
by ilyco 16 Jan '20

16 Jan '20

I ran mcmc_equilibrate on a nested block state model in a weighted graph. As per instructions, I copied the initially computed state in another object with increased hierarchy depth to 10. However, this fixed the depth to 10. Everything computed afterwards has depth 10 even if is clear that after 3 or 4 levels the nodes converge to one. There are many empty branches and when I try to plot it with empty_branches = False, I get an error stating it is not a tree. RuntimeError: Invalid hierarchical tree: No path from source to target. Did anybody perform any similar analyses? The hierarchy after mcmc_equilibrate: <NestedBlockState object, with base <BlockState object with 24 blocks (24 nonempty), degree-corrected, with 1 edge covariate, for graph <Graph object, undirected, with 230 vertices and 11230 edges, edges filtered by (<PropertyMap object with key type 'Edge' and value type 'bool', for Graph 0x7fc3a89f1210, at 0x7fc3a64911d0>, False), vertices filtered by (<PropertyMap object with key type 'Vertex' and value type 'bool', for Graph 0x7fc3a89f1210, at 0x7fc3a64912d0>, False) at 0x7fc3a89f1210>, at 0x7fc3a6491950>, and 10 levels of sizes [(230, 24), (24, 5), (5, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1)] at 0x7fc3a6491590> -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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initial minimization of nested blockmodel
by Davide Cittaro 16 Jan '20

16 Jan '20

Hello everybody, I'm new to graph-tool and nSBM, so forgive my naive question. We are still trying to understand how parameter influence our outcome. My first question is is state = gt.minimize_nested_blockmodel_dl(g) state.mcmc_sweep(niter=100) Equal to state = gt.minimize_nested_blockmodel_dl(g, mcmc_args=dict(niter=100)) ? I’m asking as the documentation executes the two steps, but the minimization function accepts parameters for MCMC sweep step. Thanks d

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Installation: Ubuntu Eoan
by Martin Helfrich 16 Jan '20

16 Jan '20

Hello everyone, could you please update the precompiled distributions under |http://downloads.skewed.de/apt/| to include a version for Ubuntu Eoan? I don't think there is one yet... This would be a great help to us. Or maybe, can I simply use another version (e.g. Disco) on my Eoan system instead? Thank you in advance! Martin

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GraphView with lambda function
by Gerion Entrup 08 Jan '20

08 Jan '20

Hi, the documentation describes that GraphView can be given a unary function to filter vertices or edges. I have tried that and it seems to fail. My GraphView has not the expected vertices and edges. However, my assumption is that the filter is only evaluated one time (at initialization). Let me make an example: ``` g = graph_tool.Graph() a = graph_tool.GraphView(g, vfilt=...) fill_the_graph(g) do_stuff_with(a) # <- here a does not contain any data ``` >From the documentation, it seems that graph_tool constructs a property from the filter function and uses this for filtering (therefore also needing O(N)), but fill this property only on construction. Can you mention this in the documentation as a hint or warning? Maybe also a recalculate function for GraphView is meaningful that evaluates the lambda function again. Best, Gerion

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all_shortest_paths
by danoli 03 Jan '20

03 Jan '20

Hi, I noticed that both shortest_path() and all_paths() give you the option to retrieve the list of edges traversed. Is there a way of obtaining the same information with all_shortest_paths()? -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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