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

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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random graph
by JM 01 Sep '21

01 Sep '21

Dear Tiago, I have a directed graph of about half a million nodes and approximately a million edges following scale free behaviour and a power law degree distribution. To test some of my hypothesis, I would like to generate random smaller graphs (about 50 up to 200 nodes) representative of the big one. When I used a sample function that samples straight away from the real distribution of the big network, I have following problems: - I generate unconnected nodes with both 0 in AND out degree. - I generate small sub parts of a few nodes that are not connected to the main graph. - If only sampling from nodes with at least 1 degree, the generated graph is coherent, but not representative anymore as I need a big portion of nodes with either only one in or one out degree. Here is the part of my script I used for that, where samples are drawn from dictionaries of the degrees: def sample_in(): a=np.random.randint(num) k_in = in_degrees[a] return k_in def sample_out(): if sample_in()==0: b=np.random.randint(num_out) k_out=out_zero_zeros.values()[b] return k_out else: b=np.random.randint(num) k_out=out_degrees[b] return k_out N=200 g=gt.random_graph(N, lambda:(sample_in(), sample_out()), model="constrained-configuration", directed=True) I also tried sampling from a list of tuples as you have mentioned before in the forum, but I didn't receive any results, as the tuples randomly drawn from my list might not be combinable. degs=[(7,1),(4,3),(5,6),(2,4),(6,8),(2,0),(3,5),(0,3),(2,7),(2,1)] g = gt.random_graph(4, lambda i: degs[i], directed=True) - Is there any option I could active that would help me in those cases I described above? - Is there a better way how to create representative small networks? Any help on that issue will be much appreciated. Best wishes, Jana -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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get_hierarchy_control_points return values
by Davide Cittaro 20 Apr '21

20 Apr '21

Hi again, I'm writing a small package that builds on graph-tool, but not on its graphics capabilities (also because I have to represent other things rather than the graph itself). Still I could use some of the functions "under the hood" for my purposes. I have a question about gt.draw.get_hierarchy_control_points(): the function returns the Bézier spline control points for edges in a given graph, but I'm having difficulties in understanding how this information is encoded. For a single edge in graph, I have dozens of values as control points (half dozens + 2), hence I suspect all splines going from node A to the root of a hierarchy and back to node B are encoded there, and control points should be taken 6 by 6 (3x2 by 3x2 coordinates?). How (x,y) for control points are encoded then: (x, x, x, y, y, y) or (x, y, x, y, x, y)? What are the 2 additiona values I have for each vector? Also, are values absolute or relative to one node in particular (A, B or root...)? Thanks d

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What formula is used for std in vertex_average and edge_average?
by VaSa 16 Jul '20

16 Jul '20

I am curious what is being used to calculate the standard deviation of the average in gt.vertex_average and gt.edge_average >>> t2=gt.Graph() >>> t2.add_vertex(2) >>> t2.add_edge(t2.vertex(0), t2.vertex(1)) >>> gt.vertex_average(t2, "in") (0.5, 0.35355339059327373) Now, shouldn't std be σ(n)=sqrt(((0-0.5)^2+(1-0.5)^2)/2)=0.5 ? also q(n-1)=sqrt((0.5^2+0.5^2)/(2-1))~=0.70710 0.3535 is sqrt(2)/4 which happens to be σ(n-1)/2, so it seems there is some relation to that. A little bigger graph. >>> t3=gt.Graph() >>> t3.add_vertex(5) >>> t3.add_edge(t3.vertex(0), t3.vertex(1)) >>> gt.vertex_average(t3, "in") (0.2, 0.17888543819998318) Now, we should have 0,1,0,0,0 series for vertex incoming degree. So Windows calc gives σ(n)=0.4 and σ(n-1)~=0.44721, so where does 0.1788854 come from ? Reason, I am asking because, I have a large graph, where the average looks quite alright but the std makes no sense, as going by the histogram, degree values are quite a bit more distributed than the std would indicate. -- 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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Block number limits in community detection with metadata
by Yan Xia 13 May '20

13 May '20

Hi, I am using the minimize_blockmodel_dl() function and the LayeredBlockState class to conduct community detection with metadata. I would like to know if it is possible to specify the block number limits separately for the user clusters and the metadata clusters (instead of the total block number limit) during the SBM inference? Since we want to have different granularity for user clusters and metadata clusters. (P.S. I tried using nested SBM and merging separately the user clusters and the metadata clusters. It worked to some extent, but it seems impossible to accurately control the final number of clusters, which we might need to.) Thanks! Yan -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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how to improve similarity for Ising model and SIS model
by gege 12 May '20

12 May '20

Hi, everyone! I met a problem when I'm learning how to use graph-tool. I read the paper, network reconstruction and community detection from dynamics, and I am trying to achieve the same result. When I followed the same settings for real networks with synthetic dynamics, their similarities were just about 0.2. I have a question about how to control the number of infection events per node,a, for the first model and the number of micro-state, M, for the second model. The whole process is shown as following. import graph_tool.all as gt from matplotlib import cm g = gt.collection.konect_data["openflights"] ## airport network with SIS dynamics gt.remove_parallel_edges(g) g = gt.extract_largest_component(g, prune=False) #simulation of an empirical dynamic model # The algorithm accepts multiple independent time-series for the # reconstruction. We will generate 100 SIS cascades starting from a # random node each time, and uniform infection probability beta=0.2. ss = [] for i in range(100): si_state = gt.SISState(g, beta=.2) s = [si_state.get_state().copy()] for j in range(10): si_state.iterate_sync() s.append(si_state.get_state().copy()) # Each time series should be represented as a single vector-valued # vertex property map with the states for each note at each time. s = gt.group_vector_property(s) ss.append(s) # Prepare the initial state of the reconstruction as an empty graph u = g.copy() u.clear_edges() ss = [u.own_property(s) for s in ss] # time series properties need to be 'owned' by graph u # Create reconstruction state rstate = gt.EpidemicsBlockState(u, s=ss, beta = None, r=1e-6, global_beta=.2, state_args=dict(B=20), nested=False, aE=g.num_edges()) # Now we collect the marginals for exactly 10,000 sweeps, at # intervals of 10 sweeps: gm = None bm = None betas = [] def collect_marginals(s): global gm, bm gm = s.collect_marginal(gm) b = gt.perfect_prop_hash([s.bstate.b])[0] bm = s.bstate.collect_vertex_marginals(bm, b=b) betas.append(s.params["global_beta"]) gt.mcmc_equilibrate(rstate, force_niter=1000, mcmc_args=dict(niter=10, xstep=0), callback=collect_marginals) print("Posterior similarity: ", gt.similarity(g, gm, g.new_ep("double", 1), gm.ep.eprob)) print("Inferred infection probability: %g ± %g" % (mean(betas), std(betas))) ########################################################## g = gt.GraphView(gt.collection.konect_data["maayan-foodweb"], directed=True)##a food web network with Ising dynamic gt.remove_parallel_edges(g) # The algorithm accepts multiple independent time-series for the # reconstruction. We will generate 1000 Ising cascades starting from a # random node each time, and the uniform inverse temperature beta=0.2. ss = [] for i in range(1000): si_state = gt.IsingGlauberState(g, beta=.1) s = [si_state.get_state().copy()] si_state.iterate_async(niter=1000) s.append(si_state.get_state().copy()) # Each time series should be represented as a single vector-valued # vertex property map with the states for each note at each time. s = gt.group_vector_property(s) ss.append(s) u = g.copy() u.clear_edges() ss = [u.own_property(s) for s in ss] rstate = gt.PseudoIsingBlockState(g,s=ss,beta=0.1,state_args=dict(B=1), nested=False, aE=g.num_edges()) gm = None bm = None betas = [] def collect_marginals(s): global gm, bm gm = s.collect_marginal(gm) b = gt.perfect_prop_hash([s.bstate.b])[0] bm = s.bstate.collect_vertex_marginals(bm, b=b) betas.append(s.params["beta"]) gt.mcmc_equilibrate(rstate, force_niter=1000, mcmc_args=dict(niter=10, xstep=0), callback=collect_marginals) print("Posterior similarity: ", gt.similarity(g, gm, g.new_ep("double", 1), gm.ep.eprob)) print("Inversed temperature: %g ± %g" % (mean(betas), std(betas))) Moreover, I also wonder how to do a nested version for the same network. Please let me know if you need more information on the question. otherwise, I hope to hear how this can be achieved using graph-tool? Thanks, Gege Hou -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Error in installing graph-tool in Ubuntu 18.04
by Narges 04 May '20

04 May '20

I followed the installation instructions in the website: https://git.skewed.de/count0/graph-tool/-/wikis/installation-instructions for installing graph-tool on Ubuntu 18.04, but when I write "sudo apt-get install python3-graph-tool" in the terminal, it gives me the following error: 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: python3-graph-tool : Depends: libboost-context1.67.0 but it is not installable Depends: libboost-iostreams1.67.0 but it is not installable Depends: libboost-python1.67.0 but it is not installable Depends: libboost-python1.67.0-py38 but it is not installable Depends: libboost-regex1.67.0-icu63 but it is not installable Depends: libc6 (>= 2.29) but 2.27-3ubuntu1 is to be installed Depends: libgcc-s1 (>= 3.4) but it is not installable Depends: libgomp1 (>= 9) but 8.4.0-1ubuntu1~18.04 is to be installed Depends: libstdc++6 (>= 9) but 8.4.0-1ubuntu1~18.04 is to be installed E: Unable to correct problems, you have held broken packages. Does anyone know how I can fix it? -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Bug in get_edges_prob for layered SBM
by anatolweg＠gmail.com 28 Apr '20

28 Apr '20

Hi, There seems to be problem with get_edges_prob for the layered SBM. Here is a minimal example: import graph_tool.all as gt import numpy as np gr=gt.generate_sbm(b=np.array([0]*500+[1]*500),probs=np.array([[10000,200],[ 200,10000]])) etype=gr.new_edge_property('int') gr.ep.etype=etype t=0 for e in gr.edges(): gr.ep.etype[e]=t%4 t+=1 state = gt.minimize_nested_blockmodel_dl(gr, deg_corr=True,layers=True,state_args=dict(ec=gr.ep.etype,layers=True)) print(state.get_edges_prob(missing=[[2,32,0]],spurious=[])) print(state.get_edges_prob(missing=[[2,32,0],[3,4,2]],spurious=[])) print(state.get_edges_prob(missing=[[2,32,0],[3,4,2],[36,7,0]],spurious=[])) pr=state.get_edges_prob(missing=[[2,32,0],[3,4,2]],spurious=gr.get_edges([gr .ep.etype])[:3]) Output: 0.0 -7.883180576649465 -7.883180576649465 --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-11-1f73a43d95dd> in <module> 12 print(state.get_edges_prob(missing=[[2,32,0],[3,4,2]],spurious=[])) 13 print(state.get_edges_prob(missing=[[2,32,0],[3,4,2],[36,7,0]],spurious=[])) ---> 14 pr=state.get_edges_prob(missing=[[2,32,0],[3,4,2]],spurious=gr.get_edges([gr .ep.etype])[:3]) /usr/lib/python3/dist-packages/graph_tool/inference/nested_blockmodel.py in get_edges_prob(self, missing, spurious, entropy_args) 499 lstate._state.clear_egroups() 500 --> 501 L += lstate.get_edges_prob(missing, spurious, entropy_args=eargs) 502 if isinstance(self.levels[0], LayeredBlockState): 503 missing = [(lstate.b[u], lstate.b[v], l_) for u, v, l_ in missing] /usr/lib/python3/dist-packages/graph_tool/inference/layered_blockmodel.py in get_edges_prob(self, missing, spurious, entropy_args) 896 if e is None: 897 raise ValueError("edge not found: (%d, %d, %d)" % \ --> 898 (int(u), int(v), l[0])) 899 900 if state.is_weighted: ValueError: edge not found: (3, 4, 2) The error occurs only when spurious edges are included but even without spurious edges the outputs above seem to be inaccurate. I tried to resolve the issue myself but couldn't make it work. Best wishes, Anatol

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performance reducing drastically as more edges are processed
by ioana 28 Apr '20

28 Apr '20

Hi, I am computing a weight for each edge, in a graph of some million nodes and about 50 mil edges. Initially, it was doing 25000 edges per minute, so it should have finished processing in about 30+ hours. Now I check the server for results after more than 72 hours, and the process is still running, but very slowly, doing less than 1000 edges per minute. Is there anything I am doing wrong, or what could be the reason for such a slowdown? I list here the code: def compute_exclusivities(g): count = 0 excl = g.new_edge_property("double") g.edge_properties["Exclusivity"] = excl edges = g.get_edges([g.edge_properties["label_id"]]) for edge in edges: edges_of_head = g.get_out_edges(edge[0], [g.edge_properties["label_id"]]) count_edges_ofhead_sametype = len(np.where(np.where(edges_of_head == edge[2])[1] == 2)[0]) edges_of_tail = g.get_in_edges(edge[1], [g.edge_properties["label_id"]]) count_edges_oftail_sametype = len(np.where(np.where(edges_of_tail == edge[2])[1] == 2)[0]) excl[g.edge(edge[0], edge[1])] = 1 / (count_edges_ofhead_sametype + count_edges_oftail_sametype - 1) count = count + 1 if count % 1000 == 0: print(count, " exclusivities computed") Thanks, Ioana -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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