graph-tool December 2021

graph-tool@skewed.de

6 participants
9 discussions

overlap/mixed-membership sbm help
by Eli Draizen 07 Mar '22

07 Mar '22

Hi everyone, I was wondering if it would be possible to provide some more examples of how to run a nested mixed membership SBM with edge weights. The new version seems to have removed the "overlap=True" option for state_args in the minimize_* functions. Is this the correct way to do it now? import graph_tool as gta > import numpy as np > g = .... # build graph > e_score = .... #Set edge weights > state_args = dict( > deg_corr=deg_corr, > base_type=gta.inference.overlap_blockmodel.OverlapBlockState, > B=2*g.num_edges(), #B_max > deg_corr=True, > recs=[e_score], > rec_types=["real-normal"]) > state = gta.inference.minimize_nested_blockmodel_dl( > g, > state_args=state_args, > multilevel_mcmc_args=dict(verbose=True)) > # improve solution with merge-split > state = state.copy(bs=state.get_bs() + [np.zeros(1)] * 4, sampling=True) for i in range(100): > if i%10==0: print(".", end="") > ret = state.multiflip_mcmc_sweep(niter=10, beta=np.inf, verbose=True) I am currently running this for a fully connected bipartite graph with 3454 nodes and 55008 edges. I understand it would take longer than the non-overlapping version, but do you have any suggestions on how to speed it up? The non-overlapping version takes about 15 minutes, while the overlapping version is still running after 1 day. Thanks for your help, Eli -- PhD Candidate, Phil Bourne's lab University of Virginia _______________________________________________ graph-tool mailing list -- graph-tool(a)skewed.de To unsubscribe send an email to graph-tool-leave(a)skewed.de

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How to control the orientation of the graph topology
by Xun Xiao 26 Feb '22

26 Feb '22

Hi Tiago, sometimes, when delete the graph and create a new one, then plot it, the plotted graph is shown upside down (in terms of the node text). So how could we control the orientation of the graph? Or which parameter we should use to always make node text shown in a correct orientation. Thanks a lot. -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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Upside down labels
by charlie 26 Feb '22

26 Feb '22

Hi, I'm suffering from the same issue mentioned in this post: https://git.skewed.de/count0/graph-tool/issues/174 Namely, I'm trying to draw a graph that includes a lot of self-looping edges, and my labels are being printed upside down. If I remove the self-loops the labels are shown the right way up. Is there a fix for it? Thanks, Charlie -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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Model specification and extraction of layer-specific partitions in LayeredBlockState when overlap=True
by arttu.malkamaki＠helsinki.fi 25 Jan '22

25 Jan '22

Hello, I'm studying political coalitions over time (three time slices) and across discrete types of relationships (collaboration, ideological similarity, and discursive similarity). Since graph-tool offers (the only) principled approach that does not require arbitrary user-specified parameters (such as 'resolution' or 'coupling'), I would very much like to use it for my analysis. I'm trying to fit a SBM to a graph with edges divided into distinct layers (three time slices X three types of relationships). Each edge has a discrete weight in the range of [0, 3], with weight referring to the 'strength' of the relationship. Not all nodes participate in all layers. I'm interested in how/if the block membership varies across layers that I expect to be somewhat dependent on one another (hence I would like to avoid fitting a separate SBM to each layer). In my ideal scenario, I would be using the NestedBlockState with LayeredBlockState as base and allowing for overlaps across layers: state=NestedBlockState(g, base_type=LayeredBlockState, state_args=dict(deg_corr=True, overlap=True, layers=True, ec=g.ep.layer, recs=[g.ep.weight], rec_types=["discrete-binomial"])) dS, nmoves=0, 0 for i in range(100): ret=state.multiflip_mcmc_sweep(niter=10) dS+=ret[0] nmoves+=ret[1] print("Change in description length:", dS) print("Number of accepted vertex moves:", nmoves) mcmc_equilibrate(state, wait=1000, mcmc_args=dict(niter=10), verbose=True) bs=[] def collect_partitions(s): global bs bs.append(s.get_bs()) mcmc_equilibrate(state, force_niter=10000, mcmc_args=dict(niter=10), verbose=True, callback=collect_partitions) pm=PartitionModeState(bs, nested=True, converge=True) pv=pm.get_marginal(g) bs=pm.get_max_nested() state=state.copy(bs=bs) Two questions: Q1) This is taking very long (days) on a high performance cluster and actually I haven't seen it finishing up for once. Is my model specification correct or simply too complex? If the latter, is there a better strategy to perform my analysis? All I can think of is to revert to a non-nested model and/or reduce the number of layers and/or edges: state=LayeredBlockState(g, deg_corr=True, overlap=True, layers=True, ec=g.ep.layer, recs=[g.ep.weight], rec_types=["discrete-binomial"]) dS, nattempts, nmoves=state.multiflip_mcmc_sweep(niter=1000) print("Change in description length:", dS) print("Number of accepted vertex moves:", nmoves) mcmc_equilibrate(state, wait=1000, nbreaks=2, mcmc_args=dict(niter=10), verbose=True) bs=[] def collect_partitions(s): global bs bs.append(s.b.a.copy()) mcmc_equilibrate(state, force_niter=10000, mcmc_args=dict(niter=10), verbose=True, callback=collect_partitions) pm=PartitionModeState(bs, converge=True) pv=pm.get_marginal(g) Q2) Say that the model runs and yields a neat NestedBlockState (or LayeredBlockState) object with X overlapping blocks, 9 layers, degree-corrected, with 2 edge covariates, for graph <Graph object, undirected, with 220 vertices and 11322 edges, 2 internal vertex properties, 4 internal edge properties>. Now, in previous posts there are mentions about the number of nodes multiplying by the number of layers when 'overlap=True', but in my experiments with only two layers, this does not seem to be the case and I struggle to find a way to extract the layer-specific block and respective memberships of nodes. I've tried iteration over vertices, but what I eventually need is a 2d_array [y=all nodes in g, x=all layers in g, values=most likely block membership] in order to map how/if the block membership of each node varies across those layers in which they participate (ideally somehow denoting non-participation in the layer with NaN or some unique value). Is there some way to get there? l=state.get_levels()[0] #NestedBlockState B=l.get_nonempty_B() bv=l.get_overlap_blocks()[0] z=np.zeros((g.num_vertices(), B)) #to be filled with most likely block membership with potential overlaps across ALL layers, but I need the layer-specific information for i in range(g.num_vertices()): z[int(i), :len(z[v])]=bv[i].a+1 for v in range(g.num_vertices()*max(g.ep.layer)+1): #to see if the number of vertices multiplied by layers, which it didn't print(bv[v]) Many thanks, Arttu

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Stop current search path in a DFS
by Gerion Entrup 13 Jan '22

13 Jan '22

Hi, I'd like to perform a DFS to find some nodes. However, I only need the first occurrences of these nodes. To visualize the problem, I have drawn a little example graph: https://i.imgur.com/BgdUzOz.png The graph is directed but has no further constraints. The search should start at the orange node. It should reveal the two nearby red nodes but not the red node labeled with "This one not". Precisely said: It should not reveal a red node that is dominated by another red node. If I would implement the DFS by myself I just wouldn't follow the outgoing edges of a red node (and stop the search for this path). However, within graph-tool I have only found a way to stop the _whole_ search with an StopSearch exception. Is there a way to use graph-tool to perform a search like I have described it? Kind regards, Gerion _______________________________________________ graph-tool mailing list -- graph-tool(a)skewed.de To unsubscribe send an email to graph-tool-leave(a)skewed.de

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Re: data structure (and graph edition)
by Lietz, Haiko 09 Dec '21

09 Dec '21

Hi Matthieu, Is this what you're looking for? https://graph-tool.skewed.de/static/doc/gt_format.html Best Haiko _______________________________________________ graph-tool mailing list -- graph-tool(a)skewed.de To unsubscribe send an email to graph-tool-leave(a)skewed.de

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data structure (and graph edition)
by Matthieu Latapy 09 Dec '21

09 Dec '21

Dear all, I am looking for information on the data structure used by graph-tool to represent graphs in cental memory; is there a documentation anywhere, or do I have to read the code? I am particularly interested in the cost of adding/removing vertices/edges in this structure. Thanks, ML -- --------------- Matthieu Latapy http://latapy.complexnetworks.fr -------------------------------- _______________________________________________ graph-tool mailing list -- graph-tool(a)skewed.de To unsubscribe send an email to graph-tool-leave(a)skewed.de

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help
by Lucas Marques 09 Dec '21

09 Dec '21

confirm e9164e3812b1de4dee957058777dd4e3bebebc1b Is it possible to find the network for a given partition from the command? The resulting vertices and edges of each group. state = gt.minimize_blockmodel_dl(G, deg_corr=False) _______________________________________________ graph-tool mailing list -- graph-tool(a)skewed.de To unsubscribe send an email to graph-tool-leave(a)skewed.de

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help
by Lucas Marques 08 Dec '21

08 Dec '21

In my network, they have a weight value for each vertex, after applying the algorithm I need to add this weight to each group in the hierarchy. So it would have a total weight of each of group B. g = gt.Graph(directed=True) dh['Cidades 1'] = g.add_vertex() g.add_edge_list(dg.values, hashed =True) weights = df['Valores'] ew = g.new_edge_property('double') ew.a = weights g.ep['edge_weight'] = ew state = gt.minimize_nested_blockmodel_dl(g, state_args=dict(recs=[g.ep.edge_weight], rec_types=["discrete-binomial"])) l: 0, N: 5550, B: 283 l: 1, N: 283, B: 57 l: 2, N: 57, B: 14 l: 3, N: 14, B: 3 l: 4, N: 3, B: 1 l: 5, N: 1, B: 1 >From the code below I have the table of the corresponding numbers of each node, I just don't know if they are in order, if it's in order with the corresponding nodes of the previous hierarchy my problem is already solved, just add the similar. For example I need to add all the weights of group 1 of hierarchy 0, then I need to do the same for group 1 of hierarchy 1, so on. Could you help me write down the weight of the group? def r(i): return levels[5].get_blocks()[i] hierarquia1 = [] for i in range(5550): hierarquia1.append(r(i)) _______________________________________________ graph-tool mailing list -- graph-tool(a)skewed.de To unsubscribe send an email to graph-tool-leave(a)skewed.de

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graph-tool December 2021