August 2020 - 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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the number of communities over time
by Jianjian 08 Sep '20

08 Sep '20

Hi, all, I am working on time-series networks. I have one network in each year, and I totally have 5 networks in 1970, 1980,1990,2000, 2010. Then I use the layered model to generate communities. state_G_layers=graph_tool.all.minimize_blockmodel_dl(G_layers,layers=True, deg_corr=True, state_args=dict(ec=G_layers.ep.layer,recs=[G_layers.ep.weight],rec_types=["real-exponential"],layers=True)) Then I got 10 communities. My questions: 1. Does it mean that in each year there are 10 communities , i.e., the number of communities remains constant over time ? 2. If so, how can we detect the change in the number of communities as the time goes by? As time goes by, some communities may disappear or merge with other communities, or the whole network becomes more homophily and forms one community. It will be appreciated if you can help me. Best regards, Jianjian -- Sent from: https://nabble.skewed.de/

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n_iter with parallel edges allowed
by Snehal Shekatkar 07 Sep '20

07 Sep '20

Hi Tiago, From the documentation for random_rewire : "If parallel_edges = False, parallel edges are not placed during rewiring. In this case, the returned graph will be a uncorrelated sample from the desired ensemble only if n_iter is sufficiently large." If I set "model = 'configuration'" and "parallel_edges = True, self_loops = True", will "n_inter = 1" suffice? Thank you SS

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Multilayer graph with select aggregation over layers
by fitzgeraldj 26 Aug '20

26 Aug '20

Hi Tiago, Thanks for an amazing library! I'm trying to use the multilayer aspect of graph-tool and I have a few questions that I haven't found answers to online: 1) How do you access parameters inferred for independent models on separate layers, say for reproducing Figure 5(b) from the paper? Unless you're just masking the graph and only drawing edges applicable to each layer? 2) I've seen you say several times that it's simple to incorporate binning of layers into the model, but I haven't been able to figure out how to do so - a brief example of how to do so would be hugely appreciated! (or some initial pointers if you're pressed for time) 3) In this binning procedure, is it possible to keep subsets of layers separate? Each of my layers is actually defined by a (property X, property Y) combo, and I would like to investigate how property X affects the grouping of property Y, as well as whether it noticeably impacts the networks structure. Apologies if I've missed the obvious! I've been trying to familiarise myself quickly with the package, and have hugely appreciated that you've coded this all yourself in the first place! Thanks, John -- Sent from: https://nabble.skewed.de/

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Layered overlapping model not working on GraphView
by fitzgeraldj 26 Aug '20

26 Aug '20

Hi there, Layered overlapping models aren't working for GraphViews, due to the following problematic lines in LayeredBlockState: if overlap and self.ec is not None: self.base_ec = self.base_g.own_property(ec.copy()) ec = agg_state.eindex.copy() pmap(ec, self.ec) <-- this line won't work as the GraphView inherits eindex self.ec = ec.copy("int") Is there an easy fix? Minimum working example below: import numpy.random as rand import graph_tool.all as gt N = 20 L = 3 tg = gt.price_network(N) # generate random network size N elayer = tg.new_ep('int') E = len(tg.get_edges()) elayer.a = rand.randint(0,high=L,size=E) # randomly assign each edge to one of L layers tg.ep['elayer'] = elayer utg = gt.GraphView(tg,rand.choice([True,False],size=N)) # take random subsample of graph stest = gt.minimize_nested_blockmodel_dl(utg,layers=True,overlap=True state_args=dict(ec=utg.ep.elayer,layers=True)) Thanks, John -- Sent from: https://nabble.skewed.de/

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distribution for highly skewed discrete edge weights
by sam 25 Aug '20

25 Aug '20

dear graph-tool mailing list, do you have any recommendations for modelling highly skewed distributions of discrete edge weights? my network is a multigraph which i collapse to a simple graph with edge-weights represent the number of edges in the multigraph between two vertices in my data, the modal edge weight is equal to 1, but the max is above 2000 if i fit a degree-corrected Poisson SBM to the multigraph, every pair of firms with a large number of edges together are grouped together in their own block. this makes sense, since the poisson model will assign very low probability to the edges for any value of a poisson parameter that can rationalize the otherwise sparse rate of edge formation. while this is not necessarily a problem per se, the large number of blocks that this creates complicates my analysis considerably, and it would be useful to use edge-covariates with a distribution that can account for the skewness to get a smaller number of blocks. wondering if Tiago or anyone else on the list can suggest any transformation-distribution combination that might help. i tried (without thinking too deeply) the transformation weight = log(weight) + 1 with real-geometric weights, but minimize_blockmodel_dl() was taking an unusually long time to fit so i escaped. the other option that came to my mind was to use a hierarchical SBM and choose a higher level where the blocks are merged. i haven't read the papers on hierarchical SBM or used them in graph-tool yet. thx, -sam -- Sent from: https://nabble.skewed.de/

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Extreme cpu usage by Eclipse PyDev project using graph-tool
by Kerr Avon 23 Aug '20

23 Aug '20

When a pydev project includes an import of graph_tool, Eclipse consumes very high cpu cycles by percentage. Open system monitor. Create the project. Open the source in the editor. On a new line, type "g.". The Eclipse java process increases to a very high percentage of cpu cycles. This might take a minute to occur. Once it happens, it continues until Eclipse is closed. The Code Analysis never seems to complete. When this problem occurred in a VirtualBox, typing became so slow as to be useless. Running in a non-virtual environment, the entire system became slow. Source code in pydev project module: from graph_tool.all import * g = Graph() v1 = g.add_vertex() v2 = g.add_vertex() e12 = g.add_edge(v1, v2) print(g) g.add OS: Ubuntu 18.04 Eclipse: Version: 2020-06 (4.16.0) Build id: 20200615-1200 PyDev for Eclipse 7.7.0.202008021154 org.python.pydev.feature.feature.group Python interpreter: python3.8 in venv (created with pipenv) graph_tool requires matplotlib, numpy, and scipy in order to prevent warnings, but these might not be required to cause the problem. <https://nabble.skewed.de/file/t496263/processes-detail-cr-2k.png> <https://nabble.skewed.de/file/t496263/resources.png> -- Sent from: https://nabble.skewed.de/

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