graph-tool September 2018

graph-tool@skewed.de

8 participants
17 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.

3 5

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.

3 3

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/

4 4

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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overlapping in nestedmodel
by fanzheng10 29 Oct '18

29 Oct '18

Hi, I am trying to enable overlap in the nested_blockmodel: state = gt.inference.minimize.minimize_nested_blockmodel_dl(g, overlap=True) So far, I have only tried a very simple network ("celegansneural"), and it gives me three levels, with the first level being a OverlapBlockState and upper levels being BlockState. [<OverlapBlockState object with 3 blocks, degree corrected, for graph <Graph object, directed, with 297 vertices and 2359 edges at 0x10bc7d710>, at 0x1351e6470>, <BlockState object with 2 blocks (2 nonempty), for graph <Graph object, directed, with 3 vertices and 6 edges at 0x135e3a1d0>, at 0x135e27ac8>, <BlockState object with 1 blocks (1 nonempty), for graph <Graph object, directed, with 2 vertices and 3 edges at 0x135214128>, at 0x1352192b0>] I am interested in inferred a DAG structure from some networks, i.e. not only the leaf nodes, but nodes on intermediate level can have multiple membership. I am wondering whether the fact that I only get one level of overlapping block is due to the very simple network, or is it simply not possible to have multiple levels of overlapping blocks? -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Problem with add_edge_list(eprop=x)
by JHickey 04 Oct '18

04 Oct '18

I'd like to use the "add_edge_list" function to add edges to my graph, and simultaneously assign a value for an edge property map for each of the added edges. When I try the following example (with no edge property map information) works fine: elist1 = np.array([[0,1], [1, 2]]) #edge list with no value for edge property map data (only the source and target for the edges) g = gt.Graph() my_eprop = g.new_edge_property('bool') g.add_edge_list(elist) But with the following example, I get the error below: elist2 = np.array([[0,1, 1], [1, 2, 0]]) #edge list with edge property map value in 3rd column g = gt.Graph() my_eprop = g.new_edge_property('bool') g.add_edge_list(elist2, eprops=my_eprop) Error: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-48-6a949ea52c9b> in <module>() ----> 1 g.add_edge_list(elist2, eprops=my_eprop) /usr/lib/python2.7/dist-packages/graph_tool/__init__.pyc in add_edge_list(self, edge_list, hashed, string_vals, eprops) 1969 eprops = () 1970 else: -> 1971 convert = [_converter(x.value_type()) for x in eprops] 1972 eprops = [_prop("e", self, x) for x in eprops] 1973 if not isinstance(edge_list, numpy.ndarray): /usr/lib/python2.7/dist-packages/graph_tool/__init__.pyc in __getitem__(self, k) 534 kt = "Graph" 535 raise ValueError("invalid key '%s' of type '%s', wanted type: %s" --> 536 % (str(k), str(type(k)), kt) ) 537 538 def __setitem__(self, k, v): ValueError: invalid key '0' of type '<type 'int'>', wanted type: Edge --------------------------------------------------------------------------- Please help! -- 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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Interactive coding with graph-tool
by Dominguez Morán, Juan Manuel 28 Sep '18

28 Sep '18

Dear graph-tool enthusiasts, the summer is clearly over in Germany since I spent my friday evening writing up a programming walkthrough that may be of interest for you :) Here's the draft: https://medium.com/@osyphys/6117f1fa1907 Let me know your feedback! Have a nice weekend, Juan Dominguez-Moran ________________________________________ Von: graph-tool <graph-tool-bounces(a)skewed.de> im Auftrag von graph-tool-request(a)skewed.de <graph-tool-request(a)skewed.de> Gesendet: Freitag, 28. September 2018 12:00 An: graph-tool(a)skewed.de Betreff: graph-tool Digest, Vol 128, Issue 9 Send graph-tool mailing list submissions to graph-tool(a)skewed.de To subscribe or unsubscribe via the World Wide Web, visit https://lists.skewed.de/mailman/listinfo/graph-tool or, via email, send a message with subject or body 'help' to graph-tool-request(a)skewed.de You can reach the person managing the list at graph-tool-owner(a)skewed.de When replying, please edit your Subject line so it is more specific than "Re: Contents of graph-tool digest..." Today's Topics: 1. Re: Problem with add_edge_list(eprop=x) (ashutosh) 2. Re: Problem with add_edge_list(eprop=x) (Tiago de Paula Peixoto) ---------------------------------------------------------------------- Message: 1 Date: Thu, 27 Sep 2018 06:03:32 -0700 (MST) From: ashutosh <ashuein(a)gmail.com> To: graph-tool(a)skewed.de Subject: Re: [graph-tool] Problem with add_edge_list(eprop=x) Message-ID: <1538053412396-0.post(a)n3.nabble.com> Content-Type: text/plain; charset=us-ascii Its' working when I changed the edge property type 'int' to 'float' for the last one. Also, the assigned property values are reversed in order when I tried the same for another example with only two property maps. So for now, the problem seems to have solved. Graph Tool version is 2.27 -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/ ------------------------------ Message: 2 Date: Fri, 28 Sep 2018 10:57:19 +0200 From: Tiago de Paula Peixoto <tiago(a)skewed.de> To: graph-tool(a)skewed.de Subject: Re: [graph-tool] Problem with add_edge_list(eprop=x) Message-ID: <3f10df20-9964-f21c-d7fc-a02fa31840a0(a)skewed.de> Content-Type: text/plain; charset=utf-8 Am 27.09.18 um 15:03 schrieb ashutosh: > Its' working when I changed the edge property type 'int' to 'float' for the > last one. I can't reproduce this. The example you gave works as expected, regardless of the property type. Note that so far you have not produced a minimal example that even allegedly reproduces the problem you are encountering. For some reason you gave us an example that _works_ instead of one that doesn't. Could you please provide a complete and minimal example that _shows_ the problem, i.e. gives the wrong output? > Also, the assigned property values are reversed in order when I tried the > same for another example with only two property maps. I can't reproduce this either. And as already explained many times, it is useless to mention problems without a reproducible example. > So for now, the problem seems to have solved. ??? If the problems you mention are really there, then they need to be fixed. I ask of you to please produce a minimal and complete example for them. Best, Tiago -- Tiago de Paula Peixoto <tiago(a)skewed.de> ------------------------------ Subject: Digest Footer _______________________________________________ graph-tool mailing list graph-tool(a)skewed.de https://lists.skewed.de/mailman/listinfo/graph-tool ------------------------------ End of graph-tool Digest, Vol 128, Issue 9 ******************************************

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graph-tool September 2018