February 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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Analysing and visualising
by Ruffle, James 24 Apr '20

24 Apr '20

Dear Graph-Tool Community, I am interested in analysing the hierarchical partitions generated by the nested blockmodel. Specifically, after I have generated a nested SBM; I would then like to post-process this and calculate measures such as eigenvector centrality for a given hierarchical node; save this as a property, and then in visualisation apply either a size or colormap constraint to said node weighted by its centrality. Using the collection data; g = gt.collection.data["celegansneural”] state = gt.minimize_nested_blockmodel_dl(g) I can then ascertain what my levels are with; l1state = state.levels[1].g l2state = state.levels[2].g etc. I can then calculate eigenvector centrality of a given hierarchical partition as follows; ee1, x1 = gt.eigenvector(l1state) ee2, x2 = gt.eigenvector(l2state) 1) This presumably then needs to be saved as a hvprops(?!). But, I am unclear how to do this, not least in a way that I know for sure that the correct hierarchical vertices within l1state and l2state are aligning to the generated centrality measures of x1 and x2, respectively. 2) Furthermore, if/when that is achieved, how can I call upon this in drawing, for example to size the level 1 hierarchical vertices according to centrality, or level 2 vertices by another measure, etc.? Hugely grateful for any solutions! James

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Problem extracting hierarchical blocks
by James Ruffle 23 Mar '20

23 Mar '20

Dear community / Tiago I have a hierarchical partition of a nested block state. The original network contained 4453 vertices and 50051 edges. state.print_summary() l: 0, N: 4453, B: 126 l: 1, N: 126, B: 46 l: 2, N: 46, B: 20 l: 3, N: 20, B: 9 l: 4, N: 9, B: 3 l: 5, N: 3, B: 1 I want to extract the community label of each vertex of each possible hierarchical level. To do this I wrote a loop based upon the guide at https://graph-tool.skewed.de/static/doc/demos/inference/inference.html Where vertexblocksdf is simply a df populated with the vertex numbers 0-4452. for idx in range(len(vertexblocksdf)): r = levels[0].get_blocks()[idx] # group membership of node idx in level 0 vertexblocksdf.ix[idx, 'level0'] = r r = levels[0].get_blocks()[r] # group membership of node idx in level 1 vertexblocksdf.ix[idx, 'level1'] = r r = levels[0].get_blocks()[r] # group membership of node idx in level 2 vertexblocksdf.ix[idx, 'level2'] = r r = levels[0].get_blocks()[r] # group membership of node idx in level 3 vertexblocksdf.ix[idx, 'level3'] = r r = levels[0].get_blocks()[r] # group membership of node idx in level 4 vertexblocksdf.ix[idx, 'level4'] = r r = levels[0].get_blocks()[r] # group membership of node idx in level 5 vertexblocksdf.ix[idx, 'level5'] = r But, I am getting strange results. My level0 column variables make sense, with 126 possibilities (as per l0 above). But my level1 column is a number between 0 and 13; of which none of my levels have 14 blocks. My level2 output is either 0 or 1, again doesn’t make sense! Level3-5 are all simply 0. *this also reproduces the same behaviour if done manually without loop. Any ideas?? James

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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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Compile/Install draw library file mis-linkings
by Kambiz Tavabi 02 Mar '20

02 Mar '20

I’ve managed to install graph-tool version 2.29 (commit d4154c6c) via aptitude installer on Ubuntu 18.04 in a python3 miniconda venv. The following example gist causes a 127 exit code caused by some sort of fudged up library linking to libgraph_tool_draw.so with: undefined symbol: _ZN5Cairo7Context16select_font_faceERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEENS_9FontSlantENS_10FontWeightE from graph_tool.all import * g = Graph() ug = Graph(directed=False) ug = Graph() ug.set_directed(False) assert ug.is_directed() == False v1 = g.add_vertex() v2 = g.add_vertex() graph_tool.draw.graph_draw(g, vertex_text=g.vertex_index, vertex_font_size=18, output_size=(200, 200), output="test.png”) Incidentally, when try to compile graph-tool from upstream master the make routine fails with a libtools error on reading libsigc-2.0.la in my configuration using --with-python-module-path=…/miniconda3/envs/mne/lib/python3.7/site-packages. On the verge of losing more hair than I can afford. Magic fix commands appreciated. -Kam

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Installation on MacOS
by lenarpooo 02 Mar '20

02 Mar '20

Dear Tiago, Thanks for building this wonderful package! As a novice in Python I'm struggling to install this package- My operating system is MacOS and I installed graph-tool via Homebrew. It seems the installation was successful. When compiling, the command './configure' doesn't work, could you please give the further instructions? Many thanks, Yingjie -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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