March 2021 - graph-tool - archives.skewed.de

How to control the orientation of the graph topology
by Xun Xiao 26 Feb '22

26 Feb '22

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. -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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Upside down labels
by charlie 26 Feb '22

26 Feb '22

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 -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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random graph
by JM 01 Sep '21

01 Sep '21

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 -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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get_hierarchy_control_points return values
by Davide Cittaro 20 Apr '21

20 Apr '21

Hi again, I'm writing a small package that builds on graph-tool, but not on its graphics capabilities (also because I have to represent other things rather than the graph itself). Still I could use some of the functions "under the hood" for my purposes. I have a question about gt.draw.get_hierarchy_control_points(): the function returns the Bézier spline control points for edges in a given graph, but I'm having difficulties in understanding how this information is encoded. For a single edge in graph, I have dozens of values as control points (half dozens + 2), hence I suspect all splines going from node A to the root of a hierarchy and back to node B are encoded there, and control points should be taken 6 by 6 (3x2 by 3x2 coordinates?). How (x,y) for control points are encoded then: (x, x, x, y, y, y) or (x, y, x, y, x, y)? What are the 2 additiona values I have for each vector? Also, are values absolute or relative to one node in particular (A, B or root...)? Thanks d

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Accessing the internal array of vector property maps
by Rodrigo Leal Cervantes 31 Mar '21

31 Mar '21

Hello world! Property maps are really useful and I've come to love them! However, sometimes it's handy to convert them back to arrays (for example for plotting). For vertex property maps that are scalar valued, we have the handy shortcut `.a` or the method `.get_array()`. However, these do not seem to work for vector valued vp's. Say for example that I'm storing the 2D coordinates of the vertices inside the vp `pos`. `g.vp.pos.a` returns `None`, and the way I typically convert this to an array is ``` print(g.vp.pos) # <VertexPropertyMap object with value type 'vector<double>', for Graph 0x7f02f04d34f0, at 0x7f0297d164f0> pos = np.array(list(g.vp.pos)) print(pos.shape) # (100, 2) ``` but I wonder if there is a better way to do this. What do other people use in this case? Rodrigo

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FYI: Macport building issue on Big Sur
by Carlos E. Andrade 29 Mar '21

29 Mar '21

Dear folks, It may not be a direct issue in graph-tool, but just for information. It does not build on Big Sur either py38, boost +python38, not py39, boost +python39. https://trac.macports.org/ticket/61583 Thanks, Carlos -- Sent from: https://nabble.skewed.de/

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Which state should I copy when doing merge-split on model with weight transformation
by jstonge 21 Mar '21

21 Mar '21

Hi all, In the following section: https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge… <https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge…> Tiago shows how to infer the best model of `foodweb_baywet` between the `real-exponential` model and the `log-normal` model, each being improved by the merge-split algorithm. My question has to do with which *state* one should copy when applying the merge-split algorithm. In the `log-normal` example model, we have: ```python y = g.ep.weight.copy() y.a = log(y.a) state_ln = gt.minimize_nested_blockmodel_dl(g, state_args=dict(recs=[y], rec_types=["real-normal"])) state_ln = state.copy(bs=state_ln.get_bs() + [np.zeros(1)] * 4, sampling=True) for i in range(100): ret = state_ln.multiflip_mcmc_sweep(niter=10, beta=np.inf) -state_ln.entropy() # ~7231 ``` But if I copy the state_ln object instead: ```python state_ln = gt.minimize_nested_blockmodel_dl(g, state_args=dict(recs=[y], rec_types=["real-normal"])) state_ln = *state_ln*.copy(bs=state_ln.get_bs() + [np.zeros(1)] * 4, sampling=True) for i in range(100): ret = state_ln.multiflip_mcmc_sweep(niter=10, beta=np.inf) -state_ln.entropy() # ~4690 ``` There is a big difference between the description length of the two models. My understanding is that the *state* in the first example comes from the previous `real-exponential` model, which means we copy its state then pass the hierarchy levels of the 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? Jonathan -- Sent from: https://nabble.skewed.de/

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Temporal community detection with DSBM
by ulgenklc 17 Mar '21

17 Mar '21

This is more of like an implementation question. I'm PhD student in math and I've been trying out different dynamic community detection(DCD) algorithms to compare their performance. I was stuck on graph-tool's implementation for a few months because the documentation for the layered networks was really short and I noticed this mailing list just now. I have multiple questions: first, am I initiating the network correctly below? My network is a list of weighted adjacency matrices corresponding to each snapshots. g = Graph() e_weight = g.new_ep("double") e_layer = g.new_ep("int") g.add_edge_list(edge_list, hashed = True, eprops=[e_weight, e_layer]) ## edge_list has (v1,v2,w,t) format g.edge_properties["edge_weight"] = e_weight ## weights of the intralayer edges g.edge_properties["edge_layer"] = e_layer ## since this is a multilayer network, each edge has to come with a layer information that it belongs to(a layer is a snapshot of the temporal network and has nothing to do with nested model which I will use the word 'level' for) Then, when I run the optimizer with the below code, I feel like I'm doing something wrong. I want to use 'independent layers' model since this is a temporal network and edge weights within each layer are varying between [0,1]. for deg_corr in [True, False]: state = minimize_nested_blockmodel_dl(g, layers = True, deg_corr = deg_corr, state_args=dict(ec = g.ep.edge_layer, layers = True, recs=[g.ep.edge_weight], rec_types=["real-exponential"])) S1 = state.entropy() # we will pad the hierarchy with another four empty levels, to # give it room to potentially increase state = state.copy(bs=state.get_bs() + [np.zeros(1)] * 4, sampling = True) for i in range(100): # this should be sufficiently large state.multiflip_mcmc_sweep(beta=np.inf, niter=10) S2 = state.entropy() print(S1,S2, S2-S1) I have a serious problem with getting the node labels. The code below only returns the node membership information for N nodes(size of the aggregated network). But since this is an evolving network, nodes are expected to change communities over time, so below code should return NxT(number of nodes times number of layers) many community labels? levels = state.get_levels() for s in levels: print(s) This returns the network partition at different levels of the nested SBM but only for N many nodes. What am I missing? -- Sent from: https://nabble.skewed.de/

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Getting shortest distances for a subset of origins - are cython-like speedups worth looking into?
by ignacioRh 16 Mar '21

16 Mar '21

Hey, Thanks a lot for the program. I'm using it on an academic project which would have died long ago without it. I'm now running some large simulations and need to speed up how I use the shortest_distance, which is in a python for loop My use case is the following: I have a large graph, and I need to compute the shortest distance between an origin subset of the nodes [A,B], to a target subset [X,Y]. So I want the distances AX, AY, BX, BY. It is infeasible to compute all pairs distances on the graph, its too large I can pass a list to shortest_distance as a target, but I can't pass a list as an origin. Hence I use list comprehension to achieve the result distances = [shortest_distances(origin, target= [X,Y] for origin in [A,B]] This is the main bottleneck in my code. I'm wondering if I can somehow put the for loop for the origins in some type of cpython extension, would that result in a speed up gain? So if put the underlying libgraph_tool_topology.get_dists() call into some cython-type code, do you think that would speed up this for loop? I'm fairly new to this type of work, and just want to see if this is a good idea to pursue, and if so, what sort of pitfalls I should keep in mind. Thanks! -- Sent from: https://nabble.skewed.de/

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Using graph-tool in Colab after Colab has upgraded to Python 3.7
by edgarbelmonte 04 Mar '21

04 Mar '21

Hi All, Wonderful to get to know about this mailing list. Thanks to the creators of graph-tool for writing a wonderful library!! I was seamlessly using graph-tool inside Colab notebooks until a week ago to visualize large graphs. But about 10 days ago Colab updated to Python 3.7 and my original graph-tool import and usage setup ran into a few hiccups due to me not knowing how to go about dealing with changes due to their update. After installing graph-tool within Colab in the usual way my main script goes as follows... ========================= from graph_tool.all import * line_no (1) for reference from gi.repository import Gtk, Gdk, GdkPixbuf, GObject, GLib line_no (2) pos= sfdp_layout(CG) line_no (30) for reference graph_draw(.. ) line_no (31) for reference ========================= The accompanying G-libs seem necessary along with graph-tool, since without line_no (2) I can't use sfdp_layout later in line_no 30. Error I get is "name 'sfdp_layout' is not defined" But if I do try import those G-libs as in line_no(2), I get an import error. "ImportError: cannot import name '_gi' from 'gi' (/usr/lib/python3/dist-packages/gi/__init__.py)" There doesn't seem to be a way to make Colab use earlier version Python 3.6 by default for a notebook. So I am a bit clueless on how to go about it. Has anyone run into similar problem? Can anyone check this out and share how one can get around this problem ? Thank you so much, Edgar -- Sent from: https://nabble.skewed.de/

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