graph-tool October 2018

graph-tool@skewed.de

10 participants
23 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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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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assigning vertex properties when adding edges
by m jadidi 04 Mar '20

04 Mar '20

Hi, I was wondering if there is any way to assign vertex properties while adding edges to the graph. for example using "add_edge_list" I can assign edge properties but later I have to iterate through all vertices again to assign their properties. I know this is not a problem when the vertex property is of the type "int" or "float" because then one can use "vprop.a = values", but in case of "string" and "object" this method doesn't work What would be the best/fastest way to handle this situation. I guess it would be very helpful to extend the "add_edge_list" function to accept vertex property in some way. cheers, -- Mohsen

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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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Weighted SBM | weight prediction
by Adrien Dulac 28 Feb '19

28 Feb '19

Dear all, I am a bit confused about the use of the weighted network models for a weight prediction task; Suppose we have a weighted network where edges are integers. We fit a SBM with a Poisson kernel as follows: |data = gt.load_graph(...) # The adjacency matrix has integer entries, and weights greater than zero are stored in data.ep.weights. state = gt.inference.minimize_blockmodel(data, B_min=10, B_max=10, state_args= {'recs':[data.ep.weights], 'rec_types' : ["discrete-poisson"]}) | My question, is how can we obtain, from |state|, a point estimate of the Poisson parameters in order to compute the distribution of the weights between pairs of nodes. Regards, Adrien Dulac

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Enforcing node separation in network partition
by ashutosh 05 Nov '18

05 Nov '18

Sir, Is there any way to enforce two nodes to be present in different network partitions rather than allowing the SBM algorithm itself to figure partitions for every node ? A little different question to the above would be, is it possible to allow partitions around specific nodes only in graph tool ? For the first one, there is an option to define the initial state in SBM algorithm, but I think that requires to define for every node. Also after the algorithm runs the network will eventually converge towards best partition which may or may not respect the condition of keeping two nodes separate. For the second, I feel it becomes like a k-means clustering. -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

2 1

Relocating graph-tool module
by Anton Ostroumov 29 Oct '18

29 Oct '18

Greetings! I recently built graph-tool for Oracle Linux and it seems to work fine (at least simple test passes). The problem Im trying to solve is to use graph-tool with Conda (or virtualenv). The build I did makes an RPM package that installs to the system directory. So, the question is: is there a way to relocate graph-tool module to another directory except completely rebuilding it? Of course I can solve Conda problem by either setting PYTHONPATH or by symlinking the module to Conda environment (or even by building Conda package), but it doesnt look right for me. Thank you!

2 1

error: not able to draw vertex index
by Madhav Vinodh Pithapuram 29 Oct '18

29 Oct '18

HI I am new to graph tools, and I have installed graph tools in anaconda environment on my PC(CentOS 7) and tried to run the following example code: *>>>from graph_tool.all import *>>>g = Graph()>>>v1 = g.add_vertex()>>>v2 = g.add_vertex()>>>e = g.add_edge(v1, v2)>>>graph_tool.draw.graph_draw(g, **vertex_text=g.vertex_index, vertex_font_size=18,output_size=(200, 200), output="two-nodes.png")* and I get his error /home/maddy/anaconda2/bin/python: symbol lookup error: /home/maddy/anaconda2/lib/python2.7/site-packages/graph_tool/draw/libgraph_tool_draw.so: undefined symbol: _ZN5Cairo7Context16select_font_faceERKSsNS_9FontSlantENS_10FontWeightE however i don't get the error if i don't use: *vertex_text=g.vertex_index in graph_draw* and the code successfully gives expected graph except for vertex_index Work-around: Although I found from the forums that It could be due to LD_LIBRARY_PATH and I have set the path in ~/.bashrc: *export LD_LIBRARY_PATH:"/home/maddy/anaconda2/lib:$LD_LIBRARY_PATH"* Could you you help me fix the issue as I am in dire need to generate the graphs for academic purposes and deadlines are just around. Thank you for the patience Madhav

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graph-tool October 2018