graph-tool April 2018

graph-tool@skewed.de

10 participants
20 discussions

boost dependencies while installing gt on Ubuntu 18.04
by Snehal Shekatkar 01 May '18

01 May '18

Hello Tiago, I am trying to install graph-tool on Ubuntu 18.04. I am encountering problems related to boost. What could be done? Reading package lists... Done Building dependency tree Reading state information... Done Some packages could not be installed. This may mean that you have requested an impossible situation or if you are using the unstable distribution that some required packages have not yet been created or been moved out of Incoming. The following information may help to resolve the situation: The following packages have unmet dependencies: python-graph-tool : Depends: libboost-graph1.65.0 but it is not installable Depends: libboost-iostreams1.65.0 but it is not installable Depends: libboost-python1.65.0 but it is not installable Depends: libboost-system1.65.0 but it is not installable Depends: libboost-thread1.65.0 but it is not installable Depends: libboost-regex1.65.0 but it is not installable Depends: libcgal12 but it is not installable E: Unable to correct problems, you have held broken packages. Sent with [ProtonMail](https://protonmail.com) Secure Email.

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Label components after filtering
by Andre Vieira 26 Apr '18

26 Apr '18

Hi, I am trying to label the components of an undirected graph after filtering, but it does not seem to work. Here is a simple example: ---------------------- from graph_tool.all import * Lx = 5 Ly = Lx g = lattice([Lx,Ly]) edges = list(g.edges()) g.remove_edge(edges[4]) g.remove_edge(edges[5]) g.remove_edge(edges[10]) g.remove_edge(edges[13]) g.remove_edge(edges[19]) g.remove_edge(edges[21]) g.remove_edge(edges[22]) g.remove_edge(edges[23]) g.remove_edge(edges[26]) g.remove_edge(edges[35]) g.remove_edge(edges[37]) lscc = label_largest_component(g) gaux = GraphView(g,vfilt=lscc) comp, hist = label_components(gaux) print comp.a ---------------------- The output of the last line is simply a vector containing 25 0's, as if none of the vertices belonged to the largest component, which is not the case, as it can be seen in the attached graph drawing. I found a workaround by creating a new graph and adding to it all the edges of the filtered graph. Was the original approach supposed to work and I am doing something silly? Cheers, Andre Vieira.

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Testing if a component is 2-edge-connected and/or identifying bridges
by Andre Vieira 25 Apr '18

25 Apr '18

Hi, is there a way to use the graph-tool algorithms to test whether a connected component of an undirected graph is 2-edge-connected, i.e., none of its edges is a bridge which, if removed, would disconnect the component? Of course one can do that by filtering out edge by edge and labeling the components of the resulting graphs, but that is probably not the most efficient way. Cheers, Andre Vieira.

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best way to identify groups in weighted graph
by M. Vigouroux 25 Apr '18

25 Apr '18

Good evening, My interrogation is quite vague… Also the answer is clearly data dependant and so it may be irritating. If so I apologize for it. I am trying to find a suitable way to identify groups in a graph with nodes connected with weighted edges. As I want the process to be as “flexible“ as possible I planed to use MCL algorithm (https://micans.org/mcl/). Also I planed to neglect weak weghted edges (with an offset) befor perfoming the calculation an set a limite to the number of Expantion/Inflation process... I wonder if you have any advice better idea or warning with this ? Thanks / Regards -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Choice of hierarchy level augmentation in cookbook
by P-M 24 Apr '18

24 Apr '18

Hi Tiago, In your cookbook examples on hierarchical SBMs I have noticed that frequently you augment the hierarchy to ten levels. Is there a particular reason for that choice? Best wishes, Philipp -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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"Segmentation fault (core dumped)" and "Aborted (core dumped)"
by P-M 24 Apr '18

24 Apr '18

Hello, I have been running a piece of code from the cookbook on a dataset of mine. I last ran it a year ago when it worked fine, now I however am getting error messages. The first one is "Segmentation fault (core dumped)" which I think is happening during a call to "state = gt.minimize_nested_blockmodel_dl(g, deg_corr=False,overlap=True)". The other one is "terminate called after throwing an instance of 'std::length_error' what(): vector::_M_default_append Aborted (core dumped)" which seems to be happening during "gt.mcmc_equilibrate(state,force_niter=20000, mcmc_args=dict(niter=10),callback=collect_marginals)". I am running the latest apt-get version of graph-tool on python2 on Ubuntu 16.04. I realise this may not be worth much without the data set (which I unfortunately can't share) but I thought I would check whether anybody has observed similar issue (my theory is that given it has worked in the past it might be linked to a version update). I will see if I can get a dataset for an MWE in the meantime. The code I was running was: import graph_tool.all as gt import numpy as np import cPickle as pickle import timeit g = gt.load_graph('graph_no_multi_1930.gt') with open('model_selection_results_1930.dat','a') as output: deg_corr = False overlap = True nL = 10 # Initialize the Markov chain from the "ground state" state = gt.minimize_nested_blockmodel_dl(g, deg_corr=deg_corr,overlap=overlap) dl = state.entropy() temp = "Description length: " temp+= str(dl) temp+='\n' output.write(temp) output.flush() print 'minimised state' bs = state.get_bs() # Get hierarchical partition. bs += [np.zeros(1)] * (nL - len(bs)) # Augment it to L = 10 with # single-group levels. state = state.copy(bs=bs, sampling=True) dls = [] # description length history vm = [None] * len(state.get_levels()) # vertex marginals em = None # edge marginals def collect_marginals(s): global vm, em levels = s.get_levels() vm = [sl.collect_vertex_marginals(vm[l]) for l, sl in enumerate(levels)] em = levels[0].collect_edge_marginals(em) dls.append(s.entropy()) # Now we collect the marginal distributions for exactly 200,000 sweeps print 'equilibrating' start=timeit.default_timer() gt.mcmc_equilibrate(state, force_niter=20000, mcmc_args=dict(niter=10), callback=collect_marginals) duration=timeit.default_timer()-start print 'duration for equilibrating: ', duration S_mf = [gt.mf_entropy(sl.g, vm[l]) for l, sl in enumerate(state.get_levels())] S_bethe = gt.bethe_entropy(g, em)[0] L = -np.mean(dls) val1 = L+sum(S_mf) val2 = L + S_bethe + sum(S_mf[1:]) temp = "Model evidence for nested blockmodel, deg_corr = " temp+=str(deg_corr)+', overlap = '+str(overlap) temp+=':'+str(val1)+"(mean field)," temp+=str(val2)+"(Bethe)"+'\n' output.write(temp) output.flush() with open('raw_results.dat','w') as f: f.write('DL: ') f.write(str(L)) f.write('\n') f.write('S_mf: ') f.write(str(sum(S_mf))) f.write('\n') f.write('S_bethe: ') f.write(str(S_bethe)) f.write('\n') f.write('S_bethe + sum(S_mf[1:]): ') f.write(str(S_bethe + sum(S_mf[1:]))) Best wishes, Philipp -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

2 4

How to compile graph-tool in ubuntu
by Soumya Sarkar 21 Apr '18

21 Apr '18

I am trying to compile and install graph-tool version 2.26 in ubuntu using ./configure but I am getting following error checking for boostlib >= 1.55.0 (105500)... yes checking whether the Boost::Python library is available... yes checking whether boost_python is the correct library... no checking whether boost_python-py27 is the correct library... no checking whether boost_python-py27 is the correct library... (cached) no checking whether boost_python-py27 is the correct library... (cached) no checking whether boost_python-py35 is the correct library... no checking whether boost_python-py35 is the correct library... (cached) no checking whether boost_python-py35 is the correct library... (cached) no checking whether boost_python is the correct library... (cached) no checking whether boost_python is the correct library... (cached) no checking whether boost_python is the correct library... (cached) no checking whether boost_python3 is the correct library... no configure: error: No usable boost::python found I have tried ./configure --with-python-boost=no but have obtained the same error ... I have also added the config.log if that helps

2 1

AttributeError: 'graph_tool::Layers<graph_tool::BlockState<boost::u' object has no attribute 'couple_state'
by Zahra Sheikhbahaee 21 Apr '18

21 Apr '18

I am trying to make a layered nested network by following command lines similar to the example in the instruction (except using nested stochastic blockmodel): LayerState = minimize_nested_blockmodel_dl(g, layers=True, state_args=dict(ec=g.edge_properties["Timestamp"], layers=True), verbose=True) bs = LayerState.get_bs() bs += [np.zeros(1)] * (L - len(bs)) LayerState = LayerState.copy(bs=bs, sampling=True) dS, nmoves = LayerState.mcmc_sweep(niter=500) I got this error: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/lib/python3/dist-packages/graph_tool/inference/nested_blockmodel.py", line 577, in mcmc_sweep return self._h_sweep(lambda s, **a: s.mcmc_sweep(**a), c=c, **kwargs) File "/usr/lib/python3/dist-packages/graph_tool/inference/nested_blockmodel.py", line 500, in _h_sweep get_entropy_args(eargs)) File "/usr/lib/python3/dist-packages/graph_tool/inference/blockmodel.py", line 698, in _couple_state self._state.couple_state(state._state, entropy_args) AttributeError: 'graph_tool::Layers<graph_tool::BlockState<boost::u' object has no attribute 'couple_state' Any comment why it occurred or suggestion about how could it be fixed ? Thanks.

2 1

Drawing labels into state object
by Crimi Alessandro 12 Apr '18

12 Apr '18

Hi, I am trying to to plot a Chord diagram using the nested model, and I would like to add labels to the nodes. However the state.draw() method doesn't all directly to have passed the labels. How can I use the graph_draw() method to embed both my state and labels. labels I would like to add (here I am writing only two) are stored in the array vprop_label (loaded as a csv file or hardcoded). This is how I am collecting the data, how do I pass vprops to state alternatively?: from graph_tool.all import * import matplotlib file_name = 'filename.csv'# where I have the edges g = load_graph_from_csv(file_name, directed=False, skip_first=False, csv_options={'delimiter': ','}) vprop_label = g.new_vertex_property("string") vprop_label[0] = "vertex one" vprop_label[1] = "vertex two" vprops = {'labels' : vprop_label} state = minimize_nested_blockmodel_dl(g) state.draw(output="my_fit.svg" )

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Create an edge property map for weights
by eward 09 Apr '18

09 Apr '18

I am passing in a graphml file that has the weights of the edges. How do I create an edge_property_map to either: -pass in the sum of the weights (using different python code) of the graph to the degre_property_map? or -use the edge_property_map to apply the weights that are in the file? (Can the egde_property_map gather this information) -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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graph-tool April 2018