October 2018 - graph-tool - archives.skewed.de

Mouse events with graph-tool
by oliver_p 29 Oct '18

29 Oct '18

Hi, we are building an interactive graph and want to implement some mouse events. We found the example: https://graph-tool.skewed.de/static/doc/demos/animation/animation.html , but we still struggle. Is there an easier way then making a subclass? Like " give the draw function a self written mouse_listener as argument" ? Thanks for help! Oliver -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Centos 7 Configure can't find CGAL even when pointing to it for graph-tool2.27
by MrDistortion 29 Oct '18

29 Oct '18

Any idea what I'm doing wrong? I"m running Centos7.5 gcc version 7.3.1 20180303, Python 3.6.3 and even though i've included the cgal directory in the command it can't find it. I get the error below : checking for exit in -lboost_thread... yes checking for __gmpz_init in -lgmp... yes checking for __gmpz_init in -lgmp... (cached) yes checking whether CGAL is available in /MYINSTALL/release/cgal4.10... no checking whether CGAL is available in /usr... no checking whether CGAL is available in /usr/local... no checking whether CGAL is available in /opt... no checking whether CGAL is available in /opt/local... no configure: error: CGAL library not found. The command I'm running is: ./configure --disable-cairo --disable-sparsehash --prefix="/MYINSTALL/graphtool2.27" --with-boost="/MYINSTALL/boost1.61/" CGAL_HOME="/MYINSTALL/cgal4.10/" CGAL_LDFLAGS="-L/MYINSTALL/cgal4.10/lib64/ -lCGAL -lCGAL_Core -lgmp -lboost_thread -lpthread" LDFLAGS="-L/MYINSTALL/cgal4.10/lib64/ -Wl,-rpath-link,/MYINSTALL/boost1.61/lib" --with-cgal="/MYINSTALL/cgal4.10" What I see in the logs are : configure:21200: checking whether CGAL is available in /MYINSTALL/cgal4.10 configure:21236: g++ -o conftest -fopenmp -O3 -fvisibility=default -fvisibility-inlines-hidden -Wno-deprecated -Wall -Wextra -ftemplate-backtrace-limit=0 -DNDEBUG -I/MYINSTALL/cgal4.10/include -pthread -I/MYINSTALL/boost1.61//include -L/MYINSTALL/cgal4.10/lib64/ -Wl,-rpath,/MYINSTALL/cgal4.10/lib64 -Wl,-rpath-link,/MYINSTALL/boost1.61/lib -L/MYINSTALL/cgal4.10/lib -lCGAL -lCGAL_Core -lgmp -lboost_thread -lpthread conftest.cpp -lgmp -lgmp >&5 /opt/rh/devtoolset-7/root/usr/libexec/gcc/x86_64-redhat-linux/7/ld: cannot find -lboost_thread collect2: error: ld returned 1 exit status configure:21236: $? = 1 configure: program exited with status 1 -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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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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Graph Tool Data Export
by ashutosh 29 Oct '18

29 Oct '18

Sir, Is there any way to export the graph tool data (built or analyzed graph data, like partitioned data) into another format, say *.tsv or *.csv file? -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Understanding Multilayer Network Analysis
by ashutosh 29 Oct '18

29 Oct '18

Sir, The graph-tool software allows to define layers for any network where each layer can have different kinds of interactions of the nodes. It is assumed that, number of edges are different in each layer but they are between the nodes which are from a common set. My question is, after the graph partition has been done to find modules, does the partition algorithm considers the information from all the layers to generate a universal partition or it partitions each layer separately ? Also, if the tool partitions the network by considering the information from all layers then what edge information it stores finally, given that every layer had different edge information initially ? -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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GraphViz on Docker Image
by Dominguez Morán, Juan Manuel 25 Oct '18

25 Oct '18

Hi, I'm getting the following error on the image that was last updated 4 months ago (info from the docker hub): --------------------------------------------------------------------------- NameError Traceback (most recent call last) /usr/lib/python3.6/site-packages/graph_tool/draw/graphviz_draw.py in graphviz_draw(g, pos, size, pin, layout, maxiter, ratio, overlap, sep, splines, vsize, penwidth, elen, gprops, vprops, eprops, vcolor, ecolor, vcmap, vnorm, ecmap, enorm, vorder, eorder, output, output_format, fork, return_string) 319 try: --> 320 if gv_new_api: 321 gvg = libgv.agopen("G".encode("utf8"), NameError: name 'gv_new_api' is not defined During handling of the above exception, another exception occurred: NameError Traceback (most recent call last) <ipython-input-4-3c9127bf2c6b> in <module>() 3 #output_size=(1920,1080), 4 #inline=True, ----> 5 output=home+'/g.png') /usr/lib/python3.6/site-packages/graph_tool/draw/graphviz_draw.py in graphviz_draw(g, pos, size, pin, layout, maxiter, ratio, overlap, sep, splines, vsize, penwidth, elen, gprops, vprops, eprops, vcolor, ecolor, vcmap, vnorm, ecmap, enorm, vorder, eorder, output, output_format, fork, return_string) 591 if has_layout: 592 libgv.gvFreeLayout(gvc, gvg) --> 593 libgv.agclose(gvg) 594 595 if len(ret) > 1: NameError: name 'libgv' is not defined --------------------------------------------------------------------------- Before I looked at the source code I tried the possible quickfix of 'pip install graphviz', which did not help. After looking at the source code I learned why it didn't help, yet dunno right out of the bat how to fix this error. Any ideas? Would greatly appreciate it! Kind regards, Juan

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More Effective Way of Creating Weighted Graphs
by Ozgun Altunkaya 24 Oct '18

24 Oct '18

Hello, I have two graphs with ~65k vertices and ~26 million edges. The first one is without weights, and the second is weighted (Vertices and edges are the same.) I have no problem generating the first one. It takes about 2-3 minutes (I also generate edges, that's why it takes 2 minutes, I guess the overall process of adding edges is merely a few seconds.) Anyway, since my weighted graph has the same edges and vertices, with only weights for each edge, I don't want to generate edges from scratch. Here's the snippet: n_vertices = len(foo) weighted_graph = Graph() weighted_graph.add_vertex(n_vertices) weights = weighted_graph.new_edge_properpty("int16_t") for e in graph.edges(): # graph is my "non-weighted" graph. weight = get_weight(int(e.source())) weighted_graph.add_edge(int(e.source()), int(e.target())) weights[e] = weight When I return the above function, it is reasonably fast, however the edge indices don't match with those of graph (no weights), hence the weights are wrong. For example, the first edge of my graph is different from weighted_graph, and the corresponding weighted_graph's weight actually belongs to graph. Here is an example, graph edge_1 = [1, 40] weighted graph edge_1 = [30, 54] However the weight for weighted graph_edge_1 is actually graph edge_1's weight (probably as expected?). Is there a way to fix this? Also, I need to generate weighted_graph for at least 5-10 times, each with different weights (however the edges and vertices are remain the same, only weights change.) Is there a faster/better way to do this? I later on use this weighted_graph in a shortest path problem with negative_weights=True. Any help is is appreciated. Thanks!

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Limit the Total Number of Paths
by Ozgun Altunkaya 23 Oct '18

23 Oct '18

Hello, Is is possible to limit the total number of paths between two given vertices/nodes for a given graph? I mean, I could easily return all and select a subset of it, but it'd not be efficient I guess. Is there a way to do that without returning all paths first?

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Error while using vcmap in graph_draw
by Snehal Shekatkar 23 Oct '18

23 Oct '18

Dear Tiago, I am trying to use a matplotlib colormap for coloring vertices, and I need transparency also. import graph_tool.all as gt import matplotlib.pyplot as plt import matplotlib.cm as cm plt.switch_backend("cairo") g = gt.collection.data["karate"] position = gt.sfdp_layout(g) fig, ax = plt.subplots(1, 1) gt.graph_draw(g, pos = position, vertex_fill_color = gt.local_clustering(g), vcmap = (cm.inferno, 0.3), mplfig = ax) plt.savefig("test.pdf") I am getting the following error: Traceback (most recent call last): File "test.py", line 12, in <module> plt.savefig("test.pdf") File "/usr/lib/python3/dist-packages/matplotlib/pyplot.py", line 701, in savefig res = fig.savefig(*args, **kwargs) File "/usr/lib/python3/dist-packages/matplotlib/figure.py", line 1834, in savefig self.canvas.print_figure(fname, **kwargs) File "/usr/lib/python3/dist-packages/matplotlib/backend_bases.py", line 2267, in print_figure **kwargs) File "/usr/lib/python3/dist-packages/matplotlib/backends/backend_cairo.py", line 470, in print_pdf return self._save(fobj, 'pdf', *args, **kwargs) File "/usr/lib/python3/dist-packages/matplotlib/backends/backend_cairo.py", line 534, in _save self.figure.draw(renderer) File "/usr/lib/python3/dist-packages/matplotlib/artist.py", line 55, in draw_wrapper return draw(artist, renderer, *args, **kwargs) File "/usr/lib/python3/dist-packages/matplotlib/figure.py", line 1299, in draw renderer, self, artists, self.suppressComposite) File "/usr/lib/python3/dist-packages/matplotlib/image.py", line 138, in _draw_list_compositing_images a.draw(renderer) File "/usr/lib/python3/dist-packages/matplotlib/artist.py", line 55, in draw_wrapper return draw(artist, renderer, *args, **kwargs) File "/usr/lib/python3/dist-packages/matplotlib/axes/_base.py", line 2437, in draw mimage._draw_list_compositing_images(renderer, self, artists) File "/usr/lib/python3/dist-packages/matplotlib/image.py", line 138, in _draw_list_compositing_images a.draw(renderer) File "/usr/lib/python3/dist-packages/graph_tool/draw/cairo_draw.py", line 1565, in draw self.vorder, self.eorder, self.nodesfirst, self.kwargs) File "/usr/lib/python3/dist-packages/graph_tool/draw/cairo_draw.py", line 690, in cairo_draw vattrs, vdefaults = _attrs(vprops, "v", g, vcmap) File "/usr/lib/python3/dist-packages/graph_tool/draw/cairo_draw.py", line 484, in _attrs nattrs[int(attr)] = _prop(d, g, _convert(attr, v, cmap)) File "/usr/lib/python3/dist-packages/graph_tool/draw/cairo_draw.py", line 323, in _convert cmap, alpha = cmap ValueError: too many values to unpack (expected 2) However, if I remove vcmap argument, it draws fine. I am on Ubuntu 18.04, and the graph-tool version is 2.27. Kindly help me. Thank you Snehal Shekatkar Sent with [ProtonMail](https://protonmail.com) Secure Email.

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Fitting SBM with different priors
by Tzu-Chi Yen 18 Oct '18

18 Oct '18

Dear Tiago, I would like to fit an SBM with the /minimize_blockmodel_dl()/ function. Specifically, I would like to customize the optimization procedure with different priors for the model parameters. I am aware that /BlockState.entropy()/ returns the entropy (for fitting to SBM) with *labelled* input (partition & degree sequence), and /model_entropy()/ returns the entropy (for constructing the model) with *static* input (B, N, E). However, I don't see an argument in the /minimize_blockmodel_dl()/ function that I could enforce certain parameter priors at the first place, be it /degree_dl_kind == "uniform"/ or /degree_dl_kind == "distributed"/. Do I miss something from the documentation? For example, may I customize /state_args/ in /minimize_blockmodel_dl()/ for this purpose? Sincerely thanks, Tzu-Chi -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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