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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I've been struggling to get graph-tool installed on a standard 18.04 Ubuntu
system for the past few hours (~5) due to the CGAL dependency. I first
tried installing graph-tool from the packages, which ran fine but the
library didn't actually seem to get installed (all the python import
statements couldn't find it).
I then tried installing from source and it makes it all the way to the end
with the error:
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
However, libcgal-dev is definitely installed:
dpkg -L libcgal-dev | head -n10
/.
/usr
/usr/lib
/usr/lib/x86_64-linux-gnu
/usr/lib/x86_64-linux-gnu/cmake
/usr/lib/x86_64-linux-gnu/cmake/CGAL
/usr/lib/x86_64-linux-gnu/cmake/CGAL/CGAL_Macros.cmake
/usr/lib/x86_64-linux-gnu/cmake/CGAL/FindTBB.cmake
/usr/lib/x86_64-linux-gnu/cmake/CGAL/CGAL_Common.cmake
/usr/lib/x86_64-linux-gnu/cmake/CGAL/CGALExports-release.cmake
No amount of futzing about with the LDFLAGS/CFLAGS seems to get it to work.
Has anyone run into this before? This is probably the longest time I've
spent trying to install a package in years :(
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Hello Tiago:
While attempting to save a binary structure I encountered this error
RuntimeError: Pickling of "graph_tool.libgraph_tool_core.Vertex" instances is not enabled (http://www.boost.org/libs/python/doc/v2/pickle.html)
I don't understand why I was able to pickle.dump a stochastic block model (which contains vertices) and not this structure which contains a list of vertices.
The url given in references returns a 404
Thanks in advance
JP

When trying to import graph_tool into eclipse workbench, the library seems
to be so big (or have a circular redundancy) and never finish to index it.
I solved the problem by executing
exec("from graph_tool.all import *")
But with this i have no autocompletition and is really painful because all
the calls to the library appears as not found functions and looses all the
sense to use an IDE. Does anyone have this problem before (in eclipse or
any other IDE)?
Best,
Leandro Radusky

Hello:
In the code for 'label_largest_component()' below,
I can't make sense of the third line. What does it accomplish?
label = g.new_vertex_property("bool")
c, h = label_components(g, directed=directed)
vfilt, inv = g.get_vertex_filter()
label.fa = c.fa == h.argmax()
return label

Hello, I intend to use graph_tool for a big network, +100k nodes and very
dense.
The dataset i'm working with at the moment is ~ 40/50 GB csv containing
vertices and edges as transactions.
Is it realistic to try SBM on such graph both computationnally and would
this be theoretically useful?
If it isnt computationnally, how big can my subgraph be in order to be
feasible?
Note: I will rent a Google Cloud Platform VM to do so.
Thank you