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

Installing graph-tool in Anaconda2
by SumanBhowmick 29 Jun '18

29 Jun '18

Greetings, I just recently have installed Anaconda2 in my laptop (Mac Yosemite). Could you please help me with the knowhow of the installations of graph-tool in the Anaconda2 (Py2.7)? Thanking you in advance. Best regards -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Complexity of graphview
by P-M 26 Jun '18

26 Jun '18

Hello, If I try to filter a graph by passing a list of vertices in an ndarray as argument to GraphView is the complexity proportional to the number of vertices in the overall graph or the number of vertices in my array? Best, Philipp -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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CGAL library not found (though installed)
by Srinivasamoorthy, Ganesh (CDC/OID/NCEZID) (CTR) 25 Jun '18

25 Jun '18

I installed CGAL from source and it installed at /usr/local/include/CGAL/ Then when I try to build graph-tools (both 2.26 and the source from git) give me CGAl not found error. They do seem to be checking in the /usr/local directory but don't seem to find it..I have attached a log. Is there something I should keep in mind while installing CGAL ?

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FW: Graphtool: Finding the paths that the maximum flow algorithm produces
by Hobé Alex 25 Jun '18

25 Jun '18

Hi Tiago and everyone else on the list Thank you for your swift reply. The attached png is one example of a max flow calculation result. The flow is limited in four places in this graph. I would like to extract the order of the edges in the four paths I drew. The shortest path solution you described corresponds to path 3 in the drawing. all_shortest_paths gives my path 3 and another completely different path before crashing with a memoryError. Looking forward to hearing from you. Best, Alex ________________________________________ From: Tiago de Paula Peixoto [tiago(a)skewed.de] Sent: Thursday, March 09, 2017 18:43 To: Hobé Alex Subject: Re: Graphtool: Finding the paths that the maximum flow algorithm produces Hi Hobé, I'm not sure I understand what you want. Any path containing edges with positive flow is a "flow path". There are many of them. Which one do you want? If you just want _some_ path, just filter out the edges with zero flow and get the shortest path. But there are many other ways to proceed... Best, Tiago PS. There is a mailing list of the graph-tool project, where questions like this can be posted. It is better to use the list than to ask me directly, since it builds a repository of questions others can consult, and other people can help you as well. On 09.03.2017 17:04, Hobé Alex wrote: > Dear Mr. de Paula Peixoto > > I am using the python graphtool for my master thesis and am enjoying the > beautiful pictures it produces. > When computing the maximum flow I would like to use other edge properties > along the computed path for further calculations. > I am therefore looking for a way to find an array, which contains the edges > along a flow path, similar to the shortest_path result. > I looked through all the pages of the online tutorial, learnt a couple of > new tricks, but haven't found a way to solve this. > > I would be very thankful for any assistance you would be able to provide and > I look forward to hearing from you. > Best wishes, > > Alex Hobé -- Tiago de Paula Peixoto <tiago(a)skewed.de>

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Graph tool - computer config (GPU ~ OpenMP ~ CPU)
by M. Vigouroux 24 Jun '18

24 Jun '18

Hi, I ask myself what would be the better config to run graph tool easily with large graphs (made from 5.10^5 square sparse correlation matrix with around 2/3 sparsity) As far as I Know GPU dosn't suit to openMP. I conclue CPU multi threading is the best option but maybe I am wrong ? Regards -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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from graph_tool.all import * gives ImportError
by vineesh vs 21 Jun '18

21 Jun '18

I tried the following command in Python and ipython prompt (2.7 version) --------------------------------------------------------------------------- In [1]: from graph_tool.all import * --------------------------------------------------------------------------- Its giving the following error. How can we solve it? --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-1-100bbe2bc9d9> in <module>() ----> 1 from graph_tool.all import * /usr/lib/python2.7/dist-packages/graph_tool/__init__.py in <module>() 103 104 from .dl_import import * --> 105 dl_import("from . import libgraph_tool_core as libcore") 106 __version__ = libcore.mod_info().version 107 /usr/lib/python2.7/dist-packages/graph_tool/dl_import.pyc in dl_import(import_expr) 55 56 try: ---> 57 exec(import_expr, local_dict, global_dict) 58 finally: 59 sys.setdlopenflags(orig_dlopen_flags) # reset it to normal case to <string> in <module>() ImportError: /usr/lib/python2.7/dist-packages/graph_tool/libgraph_tool_core.so: undefined symbol: _ZN5boost9iostreams4zlib6finishE --------------------------------------------------------------------------- Thanks

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Questions on Parallelisation
by P-M 19 Jun '18

19 Jun '18

Hello Tiago, I am trying to outsource some of my calculations to the Univeristy's cluster as the compute times for some of my datasets are getting very lengthy. I had a couple of questions arising out of this and was wondering whether you had any thoughts/advice on them: 1. The University normally limits runtimes of a given job to 12h and suggests checkpointing to get around this so that the calculation can simply resume as a new job. Apparently the cluster has "DMTCP: Distributed MultiThreaded CheckPointing" installed in order to allow checkpointing "for some applications which do not have their own support for this". Is this compatible with graph-tool or are you aware of any other ways in which I could stop and restart a job? (What I am after is stopping and restarting a graph-tool function which takes more than 12h to complete as I can obviously pickle results of a calculation straightforwardly already.) If there is no way of currently doing this is this something you might consider doing at some point in the future? 2. As far as I understand using OpenMP I can only ever use a single node at a time and am thus limited by how many CPUs and how much RAM this node supplies. To be able to use more CPUs I would need to use e.g. MPI. Are there any plans to implement this at some point? (I realise this may not be straightforward but was interested in your thoughts.) 3. I would sometimes find it useful to be able to get a progress update for some of the functions to carry out a rough order-of-magnitude estimate of required compute time. Two use cases: a) If calculating the betweenness centrality for a large network this can take a long time. Having an idea of how many nodes have been covered already would be useful to extrapolate time roughly remaining to see whether the calculation is even feasible in the time available. b) If collecting data using mcmc_equilibrate it would be potentially less meaningful as the process is stochastic, however, information on how many sweeps have been completed would still be useful to give a rough estimate of whether completion will take hours, days, weeks, or months. Best wishes, Philipp -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Most efficient way to access all nodes in a block
by P-M 19 Jun '18

19 Jun '18

Hello, I have just fitted an SBM to my graph. Having run state = gt.minimize_nested_blockmodel_dl(g, deg_corr=True) I now would like to investigate the results a bit more closely. More specifically I am after the best way to access all vertices assigned to a given block. I can use get_levels() and then get_blocks() to obtain the block membership of each vertex and from that I can use find_vertex() for a given block number to find the list of all vertices in that block which I can then use to loop through them. I wonder, however, if there is a more efficient way of obtaining all vertices in a given block? My current pseudo code looks something like the following: state = gt.minimize_nested_blockmodel_dl(g, deg_corr=True) #now do something for all vertices in each of the blocks levels = state.get_levels() graphs = state.get_bstacks() #Return the nested levels as individual graphs. num_blocks = graphs[1].num_vertices() #find the number of blocks at level 0 blocks = levels[0].get_blocks() #Returns property map with block labels for each vertex. for i in range(num_blocks): #cycle through all blocks vs = gt.find_vertex(g,blocks,i) for v in vs: #cylce through all vertices in a given block do something Is there some more efficient way of doing this that I am missing? I would ideally ultimately run it after each sweep of the mcmc algorithm so would like to minimise looping that I am doing in python if graph-tool has methods for what I am doing which will, presumably, be faster. Thank you for any advice in advance! With best wishes, Philipp -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Error during make
by P-M 12 Jun '18

12 Jun '18

Hello, I am trying to compile the git version of graph-tool. I have configured it as follows: ./configure --with-boost=/opt/anaconda/anaconda3 --enable-openmp This returns a configuration summary as follows: ================================================================================ CONFIGURATION SUMMARY ================================================================================ Using python version: 2.7.14 Python interpreter: /opt/anaconda/anaconda3/bin/python Installation path: /opt/anaconda/anaconda3/lib/python2.7/site-packages/graph_tool C++ compiler (CXX): g++ -std=gnu++14 C++ compiler version: 5.4.0 Prefix: /usr/local Pkgconfigdir: ${libdir}/pkgconfig Python CPP flags: -I/opt/anaconda/anaconda3/include/python2.7 Python LD flags: -L/opt/anaconda/anaconda3/lib -lpython2.7 Boost CPP flags: -pthread -I/opt/anaconda/anaconda3/include Boost LD flags: -lboost_iostreams -lboost_python -lboost_regex -lboost_context -lboost_coroutine Numpy CPP flags: -I/opt/anaconda/anaconda3/lib/python2.7/site-packages/numpy/core/include Sparsehash CPP flags: CGAL CPP flags: -I/usr/include CGAL LD flags: -L/usr/lib -lCGAL -lCGAL_Core -lgmp -lboost_thread -lpthread Expat CPP flags: -I/usr/include Expat LD flags: -L/usr/lib -lexpat Cairomm CPP flags: -I/usr/include/cairomm-1.0 -I/usr/lib/x86_64-linux-gnu/cairomm-1.0/include -I/usr/include/cairo -I/usr/include/glib-2.0 -I/usr/lib/x86_64-linux-gnu/glib-2.0/include -I/usr/include/pixman-1 -I/usr/include/freetype2 -I/usr/include/libpng12 -I/usr/include/sigc++-2.0 -I/usr/lib/x86_64-linux-gnu/sigc++-2.0/include Cairomm LD flags: -lcairomm-1.0 -lcairo -lsigc-2.0 OpenMP compiler flags: -fopenmp OpenMP LD flags: Extra CPPFLAGS: -DNDEBUG Extra CXXFLAGS: -fopenmp -O3 -fvisibility=default -fvisibility-inlines-hidden -Wno-deprecated -Wall -Wextra -ftemplate-backtrace-limit=0 Extra LDFLAGS: Using OpenMP: yes Using sparsehash: yes Using cairo: yes ================================================================================ and the following config.log config.log <http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com…> . I then have run make -j20 This seems to fail with the following output make -j20 make all-recursive make[1]: Entering directory '/home/pmj27/graph-tool' Making all in src make[2]: Entering directory '/home/pmj27/graph-tool/src' Making all in graph make[3]: Entering directory '/home/pmj27/graph-tool/src/graph' Making all in centrality make[4]: Entering directory '/home/pmj27/graph-tool/src/graph/centrality' CXX graph_betweenness.lo CXX graph_centrality_bind.lo CXX graph_closeness.lo CXX graph_eigenvector.lo CXX graph_hits.lo CXX graph_eigentrust.lo CXX graph_pagerank.lo CXX graph_katz.lo CXX graph_trust_transitivity.lo CXXLD libgraph_tool_centrality.la make[4]: Leaving directory '/home/pmj27/graph-tool/src/graph/centrality' Making all in clustering make[4]: Entering directory '/home/pmj27/graph-tool/src/graph/clustering' CXX graph_clustering.lo CXX graph_extended_clustering.lo CXX graph_motifs.lo CXXLD libgraph_tool_clustering.la make[4]: Leaving directory '/home/pmj27/graph-tool/src/graph/clustering' Making all in correlations make[4]: Entering directory '/home/pmj27/graph-tool/src/graph/correlations' CXX graph_assortativity.lo CXX graph_correlations.lo CXX graph_correlations_imp1.lo CXX graph_correlations_bind.lo CXX graph_correlations_combined.lo CXX graph_avg_correlations.lo CXX graph_avg_correlations_imp1.lo CXX graph_avg_correlations_combined.lo In file included from /opt/anaconda/anaconda3/include/boost/multi_array.hpp:26:0, from ../../../src/graph/histogram.hh:26, from graph_correlations.hh:27, from graph_avg_correlations.hh:21, from graph_avg_correlations_combined.cc:26: /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp: In member function ‘boost::multi_array<T, NumDims, Allocator>& boost::multi_array<T, NumDims, Allocator>::resize(const boost::detail::multi_array::extent_gen<NumDims>&) [with T = double; long unsigned int NumDims = 1ul; Allocator = std::allocator<double>]’: /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp:416:31: warning: ‘new_strides.boost::array<long int, 1ul>::elems[0ul]’ may be used uninitialized in this function [-Wmaybe-uninitialized] boost::array<index,NDims> new_strides; ^ /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp: In member function ‘boost::multi_array<T, NumDims, Allocator>& boost::multi_array<T, NumDims, Allocator>::resize(const boost::detail::multi_array::extent_gen<NumDims>&) [with T = int; long unsigned int NumDims = 1ul; Allocator = std::allocator<int>]’: /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp:416:31: warning: ‘new_strides.boost::array<long int, 1ul>::elems[0ul]’ may be used uninitialized in this function [-Wmaybe-uninitialized] In file included from /opt/anaconda/anaconda3/include/boost/multi_array.hpp:26:0, from ../../../src/graph/histogram.hh:26, from graph_correlations.hh:27, from graph_avg_correlations.hh:21, from graph_avg_correlations.cc:26: /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp: In member function ‘boost::multi_array<T, NumDims, Allocator>& boost::multi_array<T, NumDims, Allocator>::resize(const boost::detail::multi_array::extent_gen<NumDims>&) [with T = double; long unsigned int NumDims = 1ul; Allocator = std::allocator<double>]’: /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp:416:31: warning: ‘new_strides.boost::array<long int, 1ul>::elems[0ul]’ may be used uninitialized in this function [-Wmaybe-uninitialized] boost::array<index,NDims> new_strides; ^ /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp: In member function ‘boost::multi_array<T, NumDims, Allocator>& boost::multi_array<T, NumDims, Allocator>::resize(const boost::detail::multi_array::extent_gen<NumDims>&) [with T = int; long unsigned int NumDims = 1ul; Allocator = std::allocator<int>]’: /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp:416:31: warning: ‘new_strides.boost::array<long int, 1ul>::elems[0ul]’ may be used uninitialized in this function [-Wmaybe-uninitialized] In file included from /opt/anaconda/anaconda3/include/boost/multi_array.hpp:26:0, from ../../../src/graph/histogram.hh:26, from graph_avg_correlations_imp1.cc:21: /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp: In member function ‘boost::multi_array<T, NumDims, Allocator>& boost::multi_array<T, NumDims, Allocator>::resize(const boost::detail::multi_array::extent_gen<NumDims>&) [with T = double; long unsigned int NumDims = 1ul; Allocator = std::allocator<double>]’: /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp:416:31: warning: ‘new_strides.boost::array<long int, 1ul>::elems[0ul]’ may be used uninitialized in this function [-Wmaybe-uninitialized] boost::array<index,NDims> new_strides; ^ /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp: In member function ‘boost::multi_array<T, NumDims, Allocator>& boost::multi_array<T, NumDims, Allocator>::resize(const boost::detail::multi_array::extent_gen<NumDims>&) [with T = long double; long unsigned int NumDims = 1ul; Allocator = std::allocator<long double>]’: /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp:416:31: warning: ‘new_strides.boost::array<long int, 1ul>::elems[0ul]’ may be used uninitialized in this function [-Wmaybe-uninitialized] /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp: In member function ‘boost::multi_array<T, NumDims, Allocator>& boost::multi_array<T, NumDims, Allocator>::resize(const boost::detail::multi_array::extent_gen<NumDims>&) [with T = long double; long unsigned int NumDims = 1ul; Allocator = std::allocator<long double>]’: /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp:416:31: warning: ‘new_strides.boost::array<long int, 1ul>::elems[0ul]’ may be used uninitialized in this function [-Wmaybe-uninitialized] /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp: In member function ‘boost::multi_array<T, NumDims, Allocator>& boost::multi_array<T, NumDims, Allocator>::resize(const boost::detail::multi_array::extent_gen<NumDims>&) [with T = long double; long unsigned int NumDims = 1ul; Allocator = std::allocator<long double>]’: /opt/anaconda/anaconda3/include/boost/multi_array/base.hpp:416:31: warning: ‘new_strides.boost::array<long int, 1ul>::elems[0ul]’ may be used uninitialized in this function [-Wmaybe-uninitialized] CXXLD libgraph_tool_correlations.la make[4]: Leaving directory '/home/pmj27/graph-tool/src/graph/correlations' Making all in draw make[4]: Entering directory '/home/pmj27/graph-tool/src/graph/draw' CXX graph_cairo_draw.lo CXX graph_tree_cts.lo CXX pycairo_aux.lo In file included from pycairo_aux.cc:20:0: pycairo_aux.cc: In function ‘PyObject* gt_PycairoContext_FromContext(cairo_t*, PyTypeObject*, PyObject*)’: /opt/anaconda/anaconda3/include/pycairo/pycairo.h:171:39: error: ‘Pycairo_CAPI’ was not declared in this scope #define PycairoContext_FromContext (Pycairo_CAPI->Context_FromContext) ^ pycairo_aux.cc:26:12: note: in expansion of macro ‘PycairoContext_FromContext’ return PycairoContext_FromContext(ctx, type, base); ^ Makefile:575: recipe for target 'pycairo_aux.lo' failed make[4]: *** [pycairo_aux.lo] Error 1 make[4]: *** Waiting for unfinished jobs.... make[4]: Leaving directory '/home/pmj27/graph-tool/src/graph/draw' Makefile:753: recipe for target 'all-recursive' failed make[3]: *** [all-recursive] Error 1 make[3]: Leaving directory '/home/pmj27/graph-tool/src/graph' Makefile:423: recipe for target 'all-recursive' failed make[2]: *** [all-recursive] Error 1 make[2]: Leaving directory '/home/pmj27/graph-tool/src' Makefile:595: recipe for target 'all-recursive' failed make[1]: *** [all-recursive] Error 1 make[1]: Leaving directory '/home/pmj27/graph-tool' Makefile:482: recipe for target 'all' failed make: *** [all] Error 2 Could somebody help me identify where I am going wrong? I am running Ubuntu 16.04 and python2.7 (Anaconda). Best, Philipp -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Re: [graph-tool] Extract largest biconnected component
by Lietz, Haiko 12 Jun '18

12 Jun '18

> But imagine a graph consisting of a 4-clique and a 5-ring overlapping in one node: > > ... > > In this case your proposed method to extract the vertices with edges labeled the most frequent number doesn't work. Is there a universal recipe? I mean, I can check the size of each bicomponent and then pick the largest one. I guess that's the universal recipe? Haiko

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