graph-tool November 2020

graph-tool@skewed.de

8 participants
10 discussions

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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Model Selection across Distributions
by kicasta 30 Nov '20

30 Nov '20

Hi all, I´d have a question regarding model selection with different distributions. When we want to decide the partition that best describes the data for a given distribution we go with that that gives the smallest entropy. However say we want to compare 2 different distributions d1 and d2 and the best fit for d1 gives an entropy value of e1 and for d2 e2 respectively. If e1 < e2, can we say that d1 describes better our data than d2? Best Regards, Enrique Castaneda -- Sent from: https://nabble.skewed.de/

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mixed-membership SBM in graph-tool?
by sam 30 Nov '20

30 Nov '20

hi, i think i understand the difference between the overlapping SBM and mixed-membership SBM (introduced by Airoldi et al, 2009). in MMSBM, a given node can be in multiple blocks, their membership is discrete (0-1) in overlapping SBM, a given node can also be a member of multiple blocks, but it with some probability (i.e., node v is in block B1 with probability 1/2, block B2 with probability 2/3, and all other blocks with probability 0). is there any way to implement MMSBM in graph-tool? also, slightly unrelated, is there any way to fit a hierarchical model with 2 levels where the top level is overlapping and the bottom level is not? cheers, -sam -- Sent from: https://nabble.skewed.de/

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How to perform edge contraction?
by jms 30 Nov '20

30 Nov '20

Hi, I came across this library looking for an efficiently implemented graph library and it looks awesome. One of the tasks I'd like to perform is efficient edge contraction, where I remove an edge from the graph and merge the two nodes joined by that edge, and then merge all the incident edges to those nodes. I'd like to do this "dynamically", where I may iterate through a list of edges and selectively collapse some of them (skipping ones which have already been merged). Any suggestions on how to perform this with graph-tools? -- Sent from: https://nabble.skewed.de/

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local dynamical probabilities without graph knowledge
by j_nets 17 Nov '20

17 Nov '20

Hi all, I've been trying to learn more about the Bayesian inference techniques introduced in https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.123.128301 and have been having much joy trying the graph-tool software - it is a fantastic tool. I have been having difficulty understanding how one would estimate both the posterior distribution of a graph and the corresponding transmission probabilities in the SI dynamics example as suggested in the Twitter example of the aforementioned paper. For example taking the example from the cookbook (which is very clear) regarding these ideas but now assigning a edge property for the transmission probabilities (although leaving them homogeneous across all edges) ##################################### ### Look at using an edge based beta value # We will first simulate the dynamics with a given network g = gt.collection.data["dolphins"] tau = g.new_ep("double", .7) # transmission probabilities ### simulate the dynamics ss = [] for i in range(100): si_state = gt.SIState(g, beta=tau) 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 node at each time. s = gt.group_vector_property(s) ss.append(s) #################################### and now considering the inference part my confusion is arising from how to assign a prior transmission value as a edge property when we are initializing the graph as empty. So below I am just changing the function collect_marginals as I want the local transmission probs (accessed through get_x() ) rather than the global beta as before and also not using a global beta in the block state. I assume here that if we were only interested in the transmisison probs i.e., not the graph posterior, then we'd assign an edge property to the graph but if we assume that we also don't know the edges I don't see how to assign the prior distribution. (I don't think beta = .7 is setting the prior to 0.7 beforehand but I may be mistaken here?) #################################### # 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 def collect_marginals(s): global gm, bs gm = s.collect_marginal(gm) bs.append(s.bstate.b.a.copy()) betas.append(s.get_x().copy()) # Create reconstruction state # but don't use a global beta and also don't state the no. of edges # note the prior beta is the same as the true one? rstate = gt.EpidemicsBlockState(u, s=ss, beta=.7, r=1e-6, nested=False) # Now we collect the marginals for exactly 100,000 sweeps, at # intervals of 10 sweeps: gm = None bs = [] betas = [] gt.mcmc_equilibrate(rstate, force_niter=10000, mcmc_args=dict(niter=10, xstep=0), callback=collect_marginals) ################################## but when I access the arrays within betas after they all just consist of 0 values (through betas[i].get_array()) which makes me think I am not doing this process correctly (the graph similarities with the posterior and actual are also negligible~0.05). Any help with this problem (I may be doing something very incorrect in the process) and how to correctly implement an edge prior without knowledge of the graph would be greatly appreciated as I would like to delve further into these techniques. -- Sent from: https://nabble.skewed.de/

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problem with graphviz_draw()
by Rolf Sander 17 Nov '20

17 Nov '20

Hello, After updating graph_tool from version 2.33 to 2.35, I cannot make plots with graphviz_draw() anymore. Here is a minimal example to reproduce the error: ################################# import graph_tool.all as gt g = gt.Graph() v0 = g.add_vertex() v1 = g.add_vertex() e01 = g.add_edge(v0,v1) g_dict = {'layout':'neato'} gt.graphviz_draw(g, gprops=g_dict) ################################# The error message is: ################################# Error: Layout type: "n" not recognized. Use one of: circo dot fdp neato nop nop1 nop2 osage patchwork sfdp twopi Error: Layout was not done Segmentation fault ################################# I suspect that somewhere the string 'neato' is converted to a single character 'n'. In my real code, I also get several warning messages quoting only the first character of miscellaneous strings. I have python3-graph-tool installed on my computer which runs on Linux Mint 19.2 Tina (based on Ubuntu 18.04 LTS, bionic). My graphviz version is 2.40.1. Can anyone reproduce this? Best regards Rolf -- ----------------------------------------------------------------------- Rolf Sander phone: [+49] 6131/305-4610 Max-Planck Institute of Chemistry email: rolf.sander(a)mpic.de PO Box 3060, 55020 Mainz, Germany homepage: www.rolf-sander.net ----------------------------------------------------------------------- https://www.encyclopedia-of-geosciences.net https://www.geoscientific-model-development.net -----------------------------------------------------------------------

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problems with installing
by Koenr 15 Nov '20

15 Nov '20

Hi, I am writing you because I experience problems with installing graph-tool I do what was said in https://git.skewed.de/count0/graph-tool/-/wikis/installation-instructions#d… I get 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-regex1.71.0-icu67 but it is not installable Recommends: libgv-python but it is not installable Recommends: python-matplotlib but it is not installable Recommends: python-cairo but it is not going to be installed Recommends: python-gi-cairo but it is not going to be installed Recommends: python-gi but it is not going to be installed E: Unable to correct problems, you have held broken packages. ------------------------------------------------------------------------------------------------- I added the following line to sources.list deb https://downloads.skewed.de/apt bullseye main Can you help with this? I use ubuntu within Windows -- Sent from: https://nabble.skewed.de/

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graph-tool November 2020