graph-tool May 2020

graph-tool@skewed.de

17 participants
26 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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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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Distance map returned from shortest_distance function misses entries of certain vertices
by Mathias Versichele 04 Jun '20

04 Jun '20

Maybe someone can help me out with this problem I posted on StackOverFlow: https://stackoverflow.com/questions/61955566/distance-map-returned-from-sho… Basically, I discovered that a call to shortest_distance returns a distance map where certain node entries are missing (I need 349, I only get 328). Weird thing is that some of the missing nodes are intermediaries for other nodes that *are* present in the distance map. The link contains a MWE, which I tested on an older py2 setup, as well as an up-to-date Docker container. Results are the same. Would be very grateful for a solution, because I need to be certain about results correctness before I can proceed with this (very fast!) library. -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Programatically determining blocks on visualization with hSBM
by timhannigan 04 Jun '20

04 Jun '20

Hi, I've been using graph-tool extensively over the past 6 months with hSBM_topic modeling (https://github.com/martingerlach/hSBM_Topicmodel; from the Gerlach, Peixoto & Altmann, 2018 paper). I have one small question that may dramatically improve the usability of this. The output showing the hierarchical community structure in the word-document network (chord diagram) is beautiful. However, is there any way to programmatically identify the blocks (blue squares), so that I can map these onto the word groups (topics) produced by the hSBM model? Thanks, -Tim Hannigan -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Reduce function on parallel edges, and more general groupby-reduce in graph-tool
by Carlo Nicolini 04 Jun '20

04 Jun '20

I have a graph in graph-tool with parallel edges. All parallel edges are assigned an `EdgePropertyMap` representing their weight with a double. I would like to return a graph where the edges are condensed into a single edge where a custom reduce operation on these weights is performed. For example: H = gt.Graph(directed=True) H.add_edge(0,1) # edge 0 H.add_edge(0,1) # edge 1 H.add_edge(0,1) # edge 2 H.add_edge(1,0) # edge 3 H.add_edge(0,2) # edge 4 ew = H.new_edge_property("double") ew[list(H.edges())[0]]=1.2 ew[list(H.edges())[1]]=2.3 ew[list(H.edges())[2]]=-4.2 ew[list(H.edges())[3]]=5.8 ew[list(H.edges())[3]]=1.0 H.ep['weights'] = ew In this case the edge (0,1), with a reduce function "sum", should have total weight 1.2+2.3-4.2= -0.7 while the remaining edges should keep the same weight (i.e. edge 3 should keep weight 5.8 and edge 4 should maintain weight 1.0). Using `gt.condensation_graph` seems to do the trick, but only sum operation can be performed. To do this we need to use `H.vertex_index` as vertex property: Hcond, prop, vcount, ecount, v_avg, edge_avg = gt.condensation_graph(H,prop=H.vertex_index, aeprops=[H.ep['weights']]) Unfortunately this function only allows sum operation to be performed. What about using a different "reduce" function, like for example max? What about operations on non-numeric types of edges? It would greatly help carrying complex operations directly in Graph-tool, even if the value type of edges property is different than numeric. More generally, what about introducing a split-apply-reduce framework to vertices and edges, or is it already possible with some modifications to `gt.condensation_graph`?

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edge weights vs multi-graphs in minimize_blockmodel_dl
by sam 22 May '20

22 May '20

How does minimize_blockmodel_dl deal with multi-graphs (graphs where multiple edges can exist between two vertices)? Is the information discarded, or is it incorporated as edge weight, and if so, how? Sorry if this is addressed in the documentation. -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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installing certain commit of graph-tool
by mvdnheuv 22 May '20

22 May '20

Hej, I was wondering if it is possible to install the graph-tool version from a certain commit. I need maximum cardinality matching with max weight and see that you implemented this very recently, after the 2.31 version was launched. I have successfully installed graph-tool through the ubuntu bash on windows but am not aware of any way to update it to a certain commit. Is there a way to do this? Thanks in advance, Milan -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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graph-tool May 2020