Hi,
I am getting the following error when using get_edges_prob() with
layered SBMs. Minimal example:
import graph_tool.all as gt
import numpy as np
gr=gt.generate_sbm(b=np.array([0]*500+[1]*500),probs=np.array([[1,200],[200,1]]))
etype=gr.new_edge_property('int')
gr.ep.etype=etype
for
Hi,
I have a question regarding the implementing partial constraints during
blockmodel minimization. By partial constraints I mean that I have a
group of vertices V1 that are pre-labeled into k clusters i.e. vertices
from V1 that are in different clusters should not mix. Then there is a
secon
Hi,
Is there a prefered way to cite /acknowledge the use of grpah-tool in a
research paper?
Regards,
A.W.
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Hi,
I was wondering if there is a way to use the subgraph_isomorphism
methods for graphs with colored edges?
Cheers
Anatol
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Hi,
Is there a fast way to obtain all instances (not only counts) of
connected subgraphs of a certain size using graph_tool? I have tried
using subgraph_isomorphism but it is quite slow. I was wondering if one
could use the motif method, which seems to be much faster, to get
instances...
Che
On 04/29/2013 05:51 PM, Tiago de Paula Peixoto wrote:
On 04/29/2013 04:18 PM, Anatol Wegner wrote:
The script:
mt=load_graph('m.xml')
import pickle
f=open('graph','w')
pickle.dump(mt,f)
f.close()
g=open('graph','r')
m=pickle.load(g)
Traceba
On 04/29/2013 02:27 PM, Tiago de Paula Peixoto wrote:
On 04/29/2013 02:16 PM, Anatol Wegner wrote:
In general which graph format is fastest when writing/reading graphs?
I've never benchmarked this extensively, but I do believe that either
graphml or gml should be the fastest.
I am
Hi all,
In general which graph format is fastest when writing/reading graphs? I
am also getting errors when I load pickled and cpickled graphs...That is
something like:
"TypeError: No registered converter was able to produce a C++ rvalue of
type std::string from this Python object of type uni