graph-tool December 2018

graph-tool@skewed.de

5 participants
13 discussions

Unable to compile graph-tool 2.27 with CGAL 4.13
by Simon Schuler 10 Dec '18

10 Dec '18

Hi, I was not able to compile graph-tool (version 2.27) with the new CGAL (v4.13) library. ...CGAL/include/CGAL/Compact_container.h:1267:8: note: template argument deduction/substitution failed: In file included from /usr/include/c++/7/memory:76:0, from /opt/dba/common/boost/include/boost/config/no_tr1/memory.hpp:21, from /opt/dba/common/boost/include/boost/get_pointer.hpp:14, from /opt/dba/common/boost/include/boost/python/object/pointer_holder.hpp:11, from /opt/dba/common/boost/include/boost/python/to_python_indirect.hpp:10, from /opt/dba/common/boost/include/boost/python/converter/arg_to_python.hpp:10, from /opt/dba/common/boost/include/boost/python/call.hpp:15, from /opt/dba/common/boost/include/boost/python/object_core.hpp:14, from /opt/dba/common/boost/include/boost/python/object.hpp:9, from ../../../src/graph/graph.hh:23, from graph_triangulation.cc:23: /usr/include/c++/7/bits/stl_function.h:356:20: note: 'const CGAL::Triangulation_vertex_base_3<CGAL::Epick, CGAL::Triangulation_ds_vertex_base_3<CGAL::Triangulation_data_structure_3<CGAL::Triangulation_vertex_base_3<CGAL::Epick, CGAL::Triangulation_ds_vertex_base_3<void> >, CGAL::Delaunay_triangulation_cell_base_3<CGAL::Epick, CGAL::Triangulation_cell_base_3<CGAL::Epick, CGAL::Triangulation_ds_cell_base_3<void> > >, CGAL::Sequential_tag> > >' is not derived from 'const CGAL::internal::CC_iterator<DSC, Const1>' { return __x == __y; } ~~~~^~~~~~ Makefile:569: recipe for target 'graph_triangulation.lo' failed make[4]: *** [graph_triangulation.lo] Error 1 make[4]: Leaving directory '/tmp/workspace/graph-tool-2.27/src/graph/generation' Makefile:752: recipe for target 'all-recursive' failed make[3]: *** [all-recursive] Error 1 make[3]: Leaving directory '/tmp/workspace/graph-tool-2.27/src/graph' Makefile:422: recipe for target 'all-recursive' failed make[2]: *** [all-recursive] Error 1 make[2]: Leaving directory '/tmp/workspace/graph-tool-2.27/src' Makefile:594: recipe for target 'all-recursive' failed make[1]: *** [all-recursive] Error 1 make[1]: Leaving directory '/tmp/workspace/graph-tool-2.27' Makefile:481: recipe for target 'all' failed make: *** [all] Error 2 Even with the current version on Gitlab the error exists. Regards Simon

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Kernel Died Problem
by ashutosh 04 Dec '18

04 Dec '18

Sir, I am trying to follow the example on "edge prediction as binary classification". Here is my code: *import graph_tool as gt import pandas as pd* # create a graph object in data frame format *ndf = pd.DataFrame({'Node1':['a','b','c','d','e'],'Node2':['c','e','b','a','d'],'Weight':[0.2,0.8,0.4,0.5,0.7], 'RandProp1':[1,2,3,1,2]}) ng = gt.Graph() nprop = ng.new_edge_property("float") ng.edge_properties['Weight'] = nprop* # important to map the properties to the graph *LayerProp = ng.new_edge_property('float') ng.edge_properties['LayerProp'] = LayerProp* *nvp = ng.add_edge_list(ndf.values.tolist(),hashed=True,string_vals=True,eprops=[nprop,LayerProp])* # minimizing the graph and inferring partitions *stateA = gt.inference.minimize_nested_blockmodel_dl(ng,layers=True, state_args=dict(ec=LayerProp,layers=True), deg_corr=True,verbose=True)* *L = 10 bs = stateA.get_bs() bs += [np.zeros(1)]*(L-len(bs)) stateB = stateA.copy(bs=bs, sampling=True) probs=([]) def collect_edge_probs(s): p = s.get_edges_prob([missing_edges[0]],entropy_args=dict(partition_dl=False)) probs[0].append(p); missing_edges = [(1,2,1)] *# for layered network you need to specify layer number *gt.inference.mcmc_equilibrate(stateB,force_niter=1000,mcmc_args=dict(niter=10), callback=collect_edge_probs,verbose=True)* When I run this code, it gives me kernel died error. Please help. I have another query that; how can we get the layer associated with the node? In the above code when I try the command *for i in nvp: print(i)* I get the output as : *a,c,b,e,d* and when I type the command *LayerProp.a* I get the output: *PropertyArray([ 1., 2., 3., 1., 2.])* How do I understand that because the order of addition of nodes depends on the order they come along with add_edge_list command, while the LayerProp is added in the order as mentioned in the property map. -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

2 4

Structure of multilayer network
by ashutosh 03 Dec '18

03 Dec '18

Sir, I have a question regarding the structure of multilayer networks. Can we propose a multi-layer network where : a) A set of nodes are common to all the layers, say common set nodes and b) every layer has its' own set of nodes, that also interacts with the common set nodes, which may or may not be present in other layers? For example: Layer 1: Nodes: a, b, c, d Layer 2: Nodes: f, e, c, d Layer 3: Nodes: e, a, c, d Here, node 'c' and 'd' are present in all the layers. Each layer has some nodes which are not common to all the layers but present in at least two layers. Like nodes, 'a' and 'e' So, will it be correct to represent such a network using graph tool for SBM partitioning? What would define a community in such networks? I tried the same with a toy network where the final minimized state gives edges and nodes lesser than the original multigraph network. -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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graph-tool December 2018