graph-tool

graph-tool@skewed.de

1256 discussions

IMPORTANT: migration of this list to discourse
by Tiago de Paula Peixoto 08 Nov '22

08 Nov '22

Dear graph-tool users, I have decided to migrate this list to the discourse forum at: https://forum.skewed.de/c/graph-tool Discourse allows for web forums to work similarly to mailing lists, and many people nowadays consider them more friendly. All subscribed users to this list are now “staged users” of the discourse forum. This means that their identity can be claimed simply by creating an account with the same mail. Staged users do not have to create an account. They can post directly to the category by email via graph-tool(a)skewed.de. Replies will also be sent via email, which users can also reply via email — thus mimicking how a mailing list works. This is the same for all users, regardless if they are staged or not. Users can disable emails at any time if they so wish. IMPORTANT: Staged users will not receive email notifications for new topics posted by other users. If you wish this, then you need to open an account. Likewise, normal users need to “watch” the category to receive such notifications. In summary, if you do not create an account in the website you will cease to receive further notifications, until someone replies to one of your posts. (Emailing to the old mailing list has been disabled; all messages posted to graph-tool(a)skewed.de will henceforth come to discourse automatically. The mailing list archives are still available, but have been mirrored there in full.) Best, Tiago -- Tiago de Paula Peixoto <tiago(a)skewed.de>

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New discourse forum for graph-tool
by Tiago de Paula Peixoto 07 Nov '22

07 Nov '22

Dear graph-tool users, There is now an official discourse forum for graph-tool available at: https://forum.skewed.de/c/graph-tool This mailing list is going to remain active — nothing will change, but many people nowadays prefer such web discussions over mailing lists. The link to both the mailing list and forum is now available in the project's webpage. Feel free to use what you prefer. Best, Tiago -- Tiago de Paula Peixoto <tiago(a)skewed.de>

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Choosing number of blocks in each side of the bi-partite SBM
by Bernardo Modenesi 06 Nov '22

06 Nov '22

Hi, I am aware of a post asking for the same topic, but it was dated 2 years ago, but I wonder if there was any development on this front? In the function "model.minimize_nested_blockmodel_dl" it is possible to specify B_min=G and B_max=G in order to get exactly G blocks for the whole clustering. I wonder if it is possible to specify separately the number of blocks on each side of the bi-partite network, say G1 for one side of the network and G2 for the other side? Even maybe by running 2 different estimations? I also noticed that Tzu-Chi Yen has an alternative code for this, but I wonder how comparable it is to graph-tool in terms of efficiency? Code: https://github.com/junipertcy/bipartiteSBM Related doc: https://docs.netscied.tw/bipartiteSBM/usage/explore-consistency.html Thank you all!

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Building a Price Network Quickstart Error Message
by Jos J 22 Sep '22

22 Sep '22

Hello, I am going through the quickstart titled "An Example: Building a Price Network" found here https://graph-tool.skewed.de/static/doc/quickstart.html#an-example-building… After running the program, I receive the following output which is different from what the quickstart suggests should be output. Here is what I get: ------ vertex: 36063 in-degree: 0 out-degree: 1 age: 36063 vertex: 9075 in-degree: 4 out-degree: 1 age: 9075 vertex: 5967 in-degree: 3 out-degree: 1 age: 5967 vertex: 1113 in-degree: 7 out-degree: 1 age: 1113 vertex: 25 in-degree: 84 out-degree: 1 age: 25 vertex: 10 in-degree: 541 out-degree: 1 age: 10 vertex: 5 in-degree: 140 out-degree: 1 age: 5 vertex: 2 in-degree: 362 out-degree: 1 age: 2 vertex: 0 in-degree: 636 out-degree: 0 age: 0 Nowhere else to go... We found the main hub! ----- The output from "vertex: 36063 ..." down to "vertex: 5 ..." is as expected, but at "vertex: 2 ..." onward is different, and I am receiving the following error message, and the plot fails to be drawn "ValueError: 'yerr' must not contain negative values" The full error output is as follows -------------- ValueError Traceback (most recent call last) Cell In [2], line 92 89 err[err >= y] = y[err >= y] - 1e-2 91 figure(figsize=(6,4))---> 92 errorbar(in_hist[1][:-1], in_hist[0], fmt="o", yerr=err, 93 label="in") 94 gca().set_yscale("log") 95 gca().set_xscale("log") File ~/anaconda3/envs/rapids-2208/lib/python3.8/site-packages/matplotlib/pyplot.py:2480, in errorbar(x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, data, **kwargs) 2474 @_copy_docstring_and_deprecators(Axes.errorbar) 2475 def errorbar( 2476 x, y, yerr=None, xerr=None, fmt='', ecolor=None, 2477 elinewidth=None, capsize=None, barsabove=False, lolims=False, 2478 uplims=False, xlolims=False, xuplims=False, errorevery=1, 2479 capthick=None, *, data=None, **kwargs):-> 2480 return gca().errorbar( 2481 x, y, yerr=yerr, xerr=xerr, fmt=fmt, ecolor=ecolor, 2482 elinewidth=elinewidth, capsize=capsize, barsabove=barsabove, 2483 lolims=lolims, uplims=uplims, xlolims=xlolims, 2484 xuplims=xuplims, errorevery=errorevery, capthick=capthick, 2485 **({"data": data} if data is not None else {}), **kwargs) File ~/anaconda3/envs/rapids-2208/lib/python3.8/site-packages/matplotlib/__init__.py:1423, in _preprocess_data.<locals>.inner(ax, data, *args, **kwargs) 1420 @functools.wraps(func) 1421 def inner(ax, *args, data=None, **kwargs): 1422 if data is None:-> 1423 return func(ax, *map(sanitize_sequence, args), **kwargs) 1425 bound = new_sig.bind(ax, *args, **kwargs) 1426 auto_label = (bound.arguments.get(label_namer) 1427 or bound.kwargs.get(label_namer)) File ~/anaconda3/envs/rapids-2208/lib/python3.8/site-packages/matplotlib/axes/_axes.py:3587, in Axes.errorbar(self, x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, **kwargs) 3584 res = np.zeros(err.shape, dtype=bool) # Default in case of nan 3585 if np.any(np.less(err, -err, out=res, where=(err == err))): 3586 # like err<0, but also works for timedelta and nan.-> 3587 raise ValueError( 3588 f"'{dep_axis}err' must not contain negative values") 3589 # This is like 3590 # elow, ehigh = np.broadcast_to(...) 3591 # return dep - elow * ~lolims, dep + ehigh * ~uplims 3592 # except that broadcast_to would strip units. 3593 low, high = dep + np.row_stack([-(1 - lolims), 1 - uplims]) * err ValueError: 'yerr' must not contain negative values ------------------------- Any idea as to what the issue might be? I am running Python 3.8, Matplotlib 3.6.0, and graph-tool 2.45 Apologies for the previous message for the wrong library to this list, I am testing out a few libraries and hastily sent that message to the wrong project.

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Installing igraph tutorial from website: AttributeError: 'AxesSubplot' object has no attribute 'arc'
by Jos J 21 Sep '22

21 Sep '22

I have python 3.8.13, igraph 0.10.1, with matplotlib 3.6.0, and when running the sample scripts from the install page on the website, I am receiving an error. The scripts can be found here: https://igraph.org/python/tutorial/latest/install.html I am referring to the following >>> import matplotlib.pyplot as plt >>> import igraph as ig >>> fig, ax = plt.subplots() >>> g = ig.Graph.Famous("petersen") >>> ig.plot(g, target=ax) >>> plt.show() I receive the following errors at the ig.plot(g, target=ax) line: --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In [120], line 6 4 fig, ax = plt.subplots() 5 g = ig.Graph.Famous("petersen") ----> 6 ig.plot(g, target=ax) 8 plt.show() File ~/anaconda3/envs/rapids-2208/lib/python3.8/site-packages/igraph/drawing/__init__.py:245, in plot(obj, target, bbox, *args, **kwds) 243 return 244 else: --> 245 plotter( 246 backend, 247 target, 248 palette=palette, 249 *args, 250 **kwds, 251 ) 252 return target 254 # Cairo backend File ~/anaconda3/envs/rapids-2208/lib/python3.8/site-packages/igraph/drawing/graph.py:558, in __plot__(self, backend, context, *args, **kwds) 552 from igraph.drawing import DrawerDirectory 554 drawer = kwds.pop( 555 "drawer_factory", 556 DrawerDirectory.resolve(self, backend)(context), 557 ) --> 558 drawer.draw(self, *args, **kwds) File ~/anaconda3/envs/rapids-2208/lib/python3.8/site-packages/igraph/drawing/matplotlib/graph.py:233, in MatplotlibGraphDrawer.draw(self, graph, *args, **kwds) 231 drawer_method = vertex_drawer.draw 232 for vertex, visual_vertex, coords in vertex_coord_iter: --> 233 drawer_method(visual_vertex, vertex, coords) 235 # Construct the iterator that we will use to draw the vertex labels 236 vs = graph.vs File ~/anaconda3/envs/rapids-2208/lib/python3.8/site-packages/igraph/drawing/matplotlib/vertex.py:60, in MatplotlibVertexDrawer.draw(self, visual_vertex, vertex, coords) 49 width = ( 50 visual_vertex.width 51 if visual_vertex.width is not None 52 else visual_vertex.size 53 ) 54 height = ( 55 visual_vertex.height 56 if visual_vertex.height is not None 57 else visual_vertex.size 58 ) ---> 60 stroke = visual_vertex.shape.draw_path( 61 ax, 62 coords[0], 63 coords[1], 64 width, 65 height, 66 facecolor=visual_vertex.color, 67 edgecolor=visual_vertex.frame_color, 68 linewidth=visual_vertex.frame_width, 69 zorder=visual_vertex.zorder, 70 ) 71 ax.add_patch(stroke) File ~/anaconda3/envs/rapids-2208/lib/python3.8/site-packages/igraph/drawing/shapes.py:168, in CircleDrawer.draw_path(ctx, center_x, center_y, width, height, **kwargs) 166 return mpl.patches.Circle((center_x, center_y), width / 2, **kwargs) 167 else: --> 168 ctx.arc(center_x, center_y, width / 2, 0, 2 * pi) AttributeError: 'AxesSubplot' object has no attribute 'arc' --------------------------------------------- Not sure if its my python or package versions, or if its a configuration issue, or possibly an issue with igraph or matplotlib. Any advice would be most welcome

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Extra levels created by minimize_nested_blockmodel_dl
by Kevin Reuning 31 Aug '22

31 Aug '22

Hello, I am estimating a nested_block_model with bipartite data and am getting a strange result. There are about 4,000 nodes in the network and I am estimating a model by: ``` state_dc = minimize_nested_blockmodel_dl(G, state_args=dict(deg_corr=True, clabel=bipart.a, pclabel=bipart.a, recs=[G.ep.weight], rec_types=["discrete-poisson"])) for i in range(1000): # this should be sufficiently large state_dc.multiflip_mcmc_sweep(beta=np.inf, niter=10) ``` The results find 13 nested levels, but after level 5 there are only 2 nonempty blocks found in each additional level. I'm not sure if this is really a problem, or I can just ignore the levels where nothing really changes. Any advice/help would be greatly appreciated. Thank you -Kevin Kevin Reuning http://kevinreuning.com Assistant Professor, Political Science Miami University

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Calling node predicates while calculating subgraph_isomorphism?
by Sergey Mironov 31 Aug '22

31 Aug '22

Hi. I'm interested in finding subgraph isomorphisms on graphs representing tensor networks. Nodes of such graphs contain expressions (but here one could think of arithmetic expressions over +-/* and variables). Let's say I want to find an isomorphism between my graph and a pattern graph, which contains more generic expressions. Comparing expressions directly doesn't make much sense because of variables, so some form of unification[1] should be used instead. What strategy could I use to fit the existing API of subgraph_isomorphism which currently only allows me to calculate some fixed labels for nodes? Is it hard(feasible) to modify the API by adding some form of predicate comparison of nodes? BR, Sergei [1] - https://en.wikipedia.org/wiki/Unification_(computer_science)#Examples_of_sy…

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bipartite minimize_nested_blockmodel_dl() error: cannot move vertex across clabel barriers
by Siwei Zhang 25 Jul '22

25 Jul '22

Hi Dr. Peixoto, I am using graph-tool version 2.45 and I have two questions. 1. I am trying to reproduce the script in the document 2. g = gt.collection.data["celegansneural"] state = gt.minimize_nested_blockmodel_dl(g, state_args=dict(overlap=True)) and have the error: /usr/lib/python3/dist-packages/graph_tool/inference/blockmodel.py:390: UserWarning: unrecognized keyword arguments: ['overlap'] warnings.warn("unrecognized keyword arguments: " + It seems the argument of "overlap" is removed. 2. Regardless of the question1, I am trying to do a bipartite version stochastic block model and I define "clabel" to constraint labels on the vertices so that vertices with different label values will not be clustered in the same group. But I always have the error of "ValueError: cannot move vertex across clabel barriers". The below is the code: node_types = g.vp['kind'] node_types.get_array() Output: PropertyArray([1, 1, 1, ..., 2, 2, 2], dtype=int32) state = gt.minimize_nested_blockmodel_dl( g, state_args=dict(clabel=node_types,pclabel=node_types,deg_corr=True), multilevel_mcmc_args = dict(niter=niter,beta=beta)) Could you please help me with these questions? Thanks! Best, Siwei

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minimize_nested_blockmodel_dl got an unexpected keyword argument 'multilevel_mcmc_args'
by Siwei Zhang 13 Jul '22

13 Jul '22

Hello, I have a question about minimize_nested_blockmodel_dl(). The below is my code: state_args = {'clabel': node_types, 'pclabel': node_types} multilevel_mcmc_args = {'niter': niter, 'beta': beta} state = gt.minimize_nested_blockmodel_dl( g, deg_corr = True, overlap = False, state_args = state_args, multilevel_mcmc_args = multilevel_mcmc_args ) And I encountered an error that "minimize_nested_blockmodel_dl got an unexpected keyword argument 'multilevel_mcmc_args'". I removed "multilevel_mcmc_args" and it works. And I also tried example "minimize_blockmodel_dl(G, multilevel_mcmc_args=dict(B_min=4, B_max=4))" in https://git.skewed.de/count0/graph-tool/-/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! Best, Siwei

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Differential blocks between graphs
by Davide Cittaro 05 Jul '22

05 Jul '22

Hello all, I was wondering if (and possibly how) graph-tool could be used to identify differential blocks across given graphs. More specifically, if two graph have the same nodes, is it possible to identify the sets of partitions that are blocks in one graph but not the other? Would it suffice to identify partitions in both graphs and then check for the differences? Thanks d

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