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.
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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
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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
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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.
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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

I ran mcmc_equilibrate on a nested block state model in a weighted graph. As
per instructions, I copied the initially computed state in another object
with increased hierarchy depth to 10. However, this fixed the depth to 10.
Everything computed afterwards has depth 10 even if is clear that after 3 or
4 levels the nodes converge to one.
There are many empty branches and when I try to plot it with empty_branches
= False, I get an error stating it is not a tree.
RuntimeError: Invalid hierarchical tree: No path from source to target.
Did anybody perform any similar analyses?
The hierarchy after mcmc_equilibrate:
<NestedBlockState object, with base <BlockState object with 24 blocks (24
nonempty), degree-corrected, with 1 edge covariate, for graph <Graph
object, undirected, with 230 vertices and 11230 edges, edges filtered by
(<PropertyMap object with key type 'Edge' and value type 'bool', for
Graph 0x7fc3a89f1210, at 0x7fc3a64911d0>, False), vertices filtered by
(<PropertyMap object with key type 'Vertex' and value type 'bool', for Graph
0x7fc3a89f1210, at 0x7fc3a64912d0>, False) at 0x7fc3a89f1210>, at
0x7fc3a6491950>, and 10 levels of sizes [(230, 24), (24, 5), (5, 1), (1, 1),
(1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1)] at 0x7fc3a6491590>
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Hello,
I have just discovered graph-tool and read through a bunch of documentation.
I am fairly new to python and graphs. The speed comparison to networkx
really impressed me!
I was utilizing networkx for a project to find K number of weighted shortest
paths from a source to a target node.
It has 'all_simple_path' function - outputting a generator that generates
all paths starting from shortest to longest. I would simply cut the
generator to the K number of paths that I needed.
I can't find a similar function in graph-tool, but I am sure this could be
implemented. The only thing I found was the all_shortest_paths function, but
that only returns the shortest path(s), in most cases only 1. Could you
point me in the right direction how to efficiently generate and store K
number of shortest paths for a specified node?
I know I could just generate all paths and then cut down based on order, but
that's not a real solution since I am running into memory issues with a
small amount of nodes.
Thanks!
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Dear all,
I am a bit confused about the use of the weighted network models for a
weight prediction task;
Suppose we have a weighted network where edges are integers. We fit a
SBM with a Poisson kernel as follows:
|data = gt.load_graph(...) # The adjacency matrix has integer entries,
and weights greater than zero are stored in data.ep.weights. state =
gt.inference.minimize_blockmodel(data, B_min=10, B_max=10, state_args=
{'recs':[data.ep.weights], 'rec_types' : ["discrete-poisson"]}) |
My question, is how can we obtain, from |state|, a point estimate of the
Poisson parameters in order to compute the distribution of the weights
between pairs of nodes.
Regards,
Adrien Dulac

I'm having trouble with getting graphviz_draw to display the same edges as
graph_draw. graph_draw displays the correct number of edges. (Please ignore
the color differences. I'm not concerned with that just now)
Using the same graph the following calls generate graphs with different
topologies:
graph_draw(g, vertex_text=g.vp.label, output_size=(500,500))
<http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com…>
graphviz_draw(g,
#vsize=.00001,
size=(1000,1000),
ratio="auto",
layout="dot",
splines=True,
penwidth=1,
vcolor=g.vp.x_in,
ecolor=g.ep.score,
vprops={"label": g.vp.label},
vcmap=cm.OrRd,
ecmap=cm.OrRd,
eprops={
"arrowhead": "normal",
"arrowsize" : 2.0,
},
gprops={"label" : g.gp.x_in,
},
overlap="prism",
output='network.png')
<http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com…>
All filters have been cleared prior to calling these functions. Any ideas
what I'm doing wrong? I'm fairly new to using graph-tool and graphviz.
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I'm trying to reproduce the synthetic networks (graphs) described in some
papers.
How to create scale-free networks with desired power-law degree
distributions, ? The lambda λ values are given, like:
1. n = 50000, λ = 3, 2.7, 2.3, with in a paper
2. n = 4000 and λ = 2.5, or n = 6000 and λ = 3 in the other paper
Here are some references:
1. Catastrophic cascade of failures in interdependent networks, Buldyrev et
al. 2010, with a separately provided Supplementary Information
2. Small Cluster in Cyber Physical Systems, Huang et al. 2014
3. Catastrophic cascade of failures in interdependent networks, Havlin et
al. 2010, this is on the Arxiv and somewhat clarifies the first