.
(Emailing to the old mailing list has been disabled; all messages posted
to graph-tool@skewed.de will henceforth come to discourse automatically.
The mailing list archives are still available, but have been mirrored
there in full.)
Best,
Tiago
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history is available.
I'm in the process of configuring post via email, in which case the
forum will behave just like a mailing list. If this works well, I can
deprecate this list. However, in the mean-time it will remain active.
Best,
Tiago
Am 06.11.22 um 21:49 schrieb Tiago de Paula Pe
should be much
slower than graph-tool.
Best,
Tiago
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the mailing list and forum is now available in the
project's webpage.
Feel free to use what you prefer.
Best,
Tiago
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be most welcome
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Any advice/help would be greatly appreciated. Thank you
If you pass a bipartite pclabel, then this bi-partition gets reflected
in the upper levels of the hierarchy as well. This is normal.
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low, since this
predicate would need to be computed in the inner loop of the algorithm.
The hash approach outlined above is far faster, and easy to implement in
general.
Best,
Tiago
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is happening you would need to send us a
minimal but complete working example that shows the problem.
Best,
Tiago
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be more
specific? Thank you so much for your help!
This warning is not applicable if minimize_nested_blockmodel_dl() is
being used, only if you are attempting to sample from the posterior
distribution (and not finding its maximum).
Best,
Tiago
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l/-/issues/725> and I encountered
the same error. I took a look of the source code of graph-tool and I did
not find where is the problem. Could you please help me about it? Thanks!
I cannot reproduce this. What version of graph-tool are you using?
cation,
unfortunately! Hopefully in the future...
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one graph but not the other? Would it suffice to identify partitions
in both graphs and then check for the differences?
I'm not sure I completely understand. What do you mean by one block
being in one graph, and not in the other? Could you give an example?
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assigned degrees? Is it being done for
correctly sampling from a given degree distribution? Or am I missing
something?
This can be implemented in a trivial way simply by rejecting these large
numbers already in the `deg_sampler` function that you supply to
random_graph().
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, the “eweight” parameter refers to edge multiplicities, not the
covariates.
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s indeed a bug, unfortunately.
Can you please open an issue in the website with the minimal example
above so I can keep track of this and fix it?
Thanks!
Best,
Tiago
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Am 09.03.22 um 12:50 schrieb Snehal Shekatkar:
Both these operations, especially the first one, is expensive. Also,
`g.edge(u,v)` seems to return only one edge without any information
about which one it is. How can I solve this problem?
Just call: g.edge(u, v, all_edges=True)
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3/PhysRevX.11.021003
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= shortest_distance(g, source=u, pred_map=True)
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.
There is a documentation that explains how to use it, and I cannot read
your mind to determine what you did or did not understand from it.
We can only help with specific questions.
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quot;_graphml_vertex_id"]
and
g.ep["_graphml_edge_id"]
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ience function for this, which is:
state.levels[0].sample_graph()
For non-nested model this is only:
state.sample_graph()
These are just convenience wrappers for generate_sbm().
Best,
Tiago
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server [2] is empty.
Is it indeed supposed to contain a deb or is it an issue with the
documentation?
[1]:
https://git.skewed.de/count0/graph-tool/-/wikis/installation-instructions#debian-ubuntu
[2]: https://downloads.skewed.de/apt/dists/impish/
Thanks in advance
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that would be appreciated.
Best,
Tiago
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still change slightly in future releases, but
you'll always be able to download any version from PyPI.
This is very interesting! Thanks for sharing.
Best,
Tiago
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y in replying, but I don't see an obvious way to
implement what you want with the current code.
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this what you're looking for?
https://graph-tool.skewed.de/static/doc/gt_format.html
Best
Haiko
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incident on the groups. But it is still not very
clear exactly what you want to find.
In any case, this is a question about a particular research problem, so
I don't believe it is appropriate for this list, which is about using
graph-tool.
Best,
Tiago
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ll me what you mean by this.
Best,
Tiago
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ok them up, it's better just to
store their index, and get a descriptor at the time you need it.
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ot;.
Of course it does. Just reverse the graph with:
GraphView(g, reversed=True)
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Am 11.11.21 um 10:47 schrieb Monecke, Stephan:
Any ideas on how I can debug this?
Yes: try to isolate the problem by constructing a minimal,
self-contained program that reproduces the crash.
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ean-time, what you can do is to fit the non-overlapping
model first, and use that as a starting point to the MCMC with overlap.
You do that simply by doing:
state = state.copy(state_args=dict(overlap=True))
Best,
Tiago
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n the underlying graph.
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Tiago
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Am 26.10.21 um 16:23 schrieb Tiago de Paula Peixoto:
It is possible to pass a single source but multiple targets.
It is possible to pass also a single source and no target, in which case
the function computes the distances to all other vertices.
By sub-sampling the source vertices you can
V are lists of same length with sources / targets but that's not
implemented.
It is possible to pass a single source but multiple targets.
Subsampling is usually a good technique to reduce the computation time,
but it is hard to know what is applicable to you.
Best,
Tiago
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State objects being created,
together the memory usage required for the minimize_blockmodel_dl()
algorithm.
I don't think there is very much you can do, except use a model that
requires less internal book-keeping, e.g. PPBlockState.
Best,
Tiago
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tant to tell us what version of graph-tool you are running.
Best,
Tiago
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parallel using
multiprocessing. my impression is that MCMC was relatively light on memory.
As usual, it is not possible to say anything concrete without a minimal
complete working example that shows the problem.
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n_property)
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andom.seed() initializes numpy's RNG, which different from
the one used in graph-tool (which is in C++). You need to replace that
line with:
gt.seed_rng(0)
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] = g.own_property(blocks[0])
But I can only guess.
Best,
Tiago
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g.org key server. Please check your network
connectivity, try another key server, or try to download the key
directly via the browser.
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roblem to the conda-forge project:
https://github.com/conda-forge/graph-tool-feedstock/issues
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y much.
I notice that you are subclassing Graph, and probably omitting to us
specializations that you are making to Graph.vertex() and other methods.
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similar to the full network.
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e only way possible.
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ental/lib/python3.8/site-packages/graph_tool/inference/blockmodel.py
in remove_vertex(self, v)
1144twice.
1145 """
-> 1146 self._state.remove_vertex(int(v))
1147
1148 def add_vertex(self, v, r):
AttributeError: 'graph_tool::BlockS
(n)
Please read carefully the documentation to understand the difference
between G.iter_edges() and G.edges().
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the original
NestedBlockState object? How do I have to write that?
Just create a new NestedBlockState (or, equivalently, copy the from the
old one):
state = NestedBlockState(g, bs=y)
or
state = state.copy(bs=y)
Best,
Tiago
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similarity between layers.
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Am 20.08.21 um 22:09 schrieb Oliver Baumann:
Tiago de Paula Peixoto wrote:
Am 20.08.21 um 20:51 schrieb oliver.baumann(a)uni-bayreuth.de:
Hi all,
I'm currently offloading construction of the uni-modal projection of a bipartite
graph from Python to C++ (NB: I am not very familiar
not mean that the
graph is not being filtered... If you iterate over it, you will get what
you expect.
Best,
Tiago
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ted in the library.
Furthermore, I don't think that the microcanonical priors / integrated
likelihoods will be easy to write down in closed form for this kind of
constraint.
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s an array indexed by the edge index, which will contain values
for non-existing edges if the indexes are not contiguous. This is what
you are seeing.
If you want to see only the property values for existing edges, you
should get a filtered array with the ".fa" property.
Best,
Tiag
the ax sizes are the exactly the same.
Can you please help me how can I sort this out?
You can set the vertex size and edge with explicitly, by passing values
to the vertex_size and edge_penwidth parameters of the graph_draw()
function.
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these classes?
Will it work?
Yes, pickling is supported for property maps of type boost::python,
provided the values themselves can be pickled.
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mouse, I am
still able to select or highlight a filter vertex.
Is there an easy way to fully hide the filtered vertices?
Without a minimal working example that shows the problem you are
describig, there is not much that we can help with...
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since the python interpreter needs to be
involved. So it's better to do this once, ahead of time, and preferably
store all property map values in a single python object.
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es from your
C++ extension by using python::extract().
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layer 0 in the property
map "bm". Is this what you want?
Best,
Tiago
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-edge also belongs to a specific layer.
What else do you need?
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get_edge_blocks. I
presume there is a relatively straightforward way to do this?
Do what? Please ask a specific question.
Best,
Tiago
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twork!
There must be something wrong going on.
Try the step-wise approach, maybe it will help you to track it down.
Best,
Tiago
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blocks(), which will give you this
information. From this, you should be able to decompose membership
across layers, etc.
Best,
Tiago
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e the optimizations that have been
included and what do they affect
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rarchies of different depths.
Best,
Tiago
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NameError: name 'etype' is not defined
You seem unfamiliar with the Python syntax. In Python, objects need to
be declared before they are used.
The correct way to obtain the property map would have been:
etype = G.ep["etype"]
etype[e]
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ds_str] = wgts
GV2 = gt.GraphView(GV, efilt=wgts.fa != np.inf)
*3) I then use GV2 for calculating Dijkstra and such...*
I could of course work on an MWE of some sorts. But would be very
nice to get an estimate on mem footprint, and to see if I'm doing
use in your particular
circumstances, we have to know first what it is...
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ying you got a segfault is not actionable information, since we
cannot easily reproduce it.
Best,
Tiago
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Am 01.06.21 um 17:25 schrieb Davide Cittaro:
It seems this feature has already been requested :)
https://git.skewed.de/count0/graph-tool/-/issues/671
<https://git.skewed.de/count0/graph-tool/-/issues/671>
Indeed! :-)
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iates is not yet implemented!
This is not very difficult to do "by hand", bit it would be a nice
addition to the library.
Could you please open an issue in the website for this request, and I
will try to include it for the next release.
Best,
Tiago
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c/doc/demos/inference/inference.html#edge-weights-and-covariates
Best,
Tiago
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Am 07.05.21 um 20:17 schrieb Marat Idrisov:
Yes, I use NJOBS=1
I never needed 10GB to compile it with only one job... But in any case,
I'm afraid there is not much that can be done.
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in the near future or can you suggest steps to optimize the build?
Change the variable NJOBS=4 to 1. This will reduce the memory usage (but
it will increase the build time).
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Thanks for this wonderful module!
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Am 03.05.21 um 17:20 schrieb JohannHM:
Thank for your comment, Tiago.
Would you please recommended me one of the included methods of your gt that is
useful for comparing:
a. cover, vs partition
b. covers, vs covers
Nothing in particular comes to mind.
Best,
Tiago
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n, is non-overlapping.
RMI is not applicable to this case.
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r each node, indexed by the
node index.
Best,
Tiago
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m one is versus the other, when
compared to a fully random graph with the same density, but no deeper
insight.
Best,
Tiago
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solid
statistical grounds.
Best,
Tiago
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t all the partitions into the
lowest level and get the marginals for that.
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points
cts = gt.draw.get_hierarchy_control_points(g, tg, tpos)
```
This is given by the "tg" (a graph representing the hierarchy tree) and
"tpos" parameters above (the position of the tree nodes).
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quot;
way, without plotting support.
It's perfectly possible. GTK is an optional run-time dependency. It does
not have to be present during compilation, and if it's not available at
run time, the graph-tool import will still succeed, with only a warning
that graph_draw() will not work.
e" version of the model, then the nodes of
degree zero in a given layer are considered not to be long to that
layer, i.e. they have a probability of zero of receiving edges of that type.
Best,
Tiago
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ng I intended to fix,
but it seems I forgot.)
Best,
Tiago
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meaningful. From the point of view of model
selection, the description length can only be used to compare identical
networks.
Before you conduct your analysis, you should have a clear criterion of
what you consider a "better" partition.
Best,
Tiago
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, selected.get_blocks()))
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ns you can do:
a = g.vp.pos.get_2d_array([0, 1])
The reason why this is needed is because vector properties are *not*
internally stored as contiguous 2D arrays.
Best,
Tiago
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state_ln model. Is it supposed to be so?
Shouldn't we always copy the state of the models we ran in the first place
to run merge-split algorithm?
Yes, this is an error in the howto! Thanks for noticing. It will be
fixed in the next version.
Best,
Tiago
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s() call into some
cython-type code, do you think that would speed up this for loop?
If the bottleneck is really there, this could help, but not by much. But
again, I think it's unlikely, unless your graph is quite small.
Best,
Tiago
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un into similar problem? Can anyone check this out and share how
one can get around this problem ?
Thank you so much,
Edgar
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ins all the
answers to questions such as this:
https://graph-tool.skewed.de/static/doc/inference.html#graph_tool.inference.blockmodel.BlockState.get_nonempty_B
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passing overlap=True to
LayeredBLockState), which will cluster the "half-edges", and hence give
partitions that (potentially) vary between the layers.
Best,
Tiago
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