graph-tool February 2017

graph-tool@skewed.de

13 participants
30 discussions

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.

3 5

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.

3 3

What formula is used for std in vertex_average and edge_average?
by VaSa 16 Jul '20

16 Jul '20

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. -- 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.

3 3

assigning vertex properties when adding edges
by m jadidi 04 Mar '20

04 Mar '20

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

3 4

Problem with add_edge_list(eprop=x)
by JHickey 04 Oct '18

04 Oct '18

I'd like to use the "add_edge_list" function to add edges to my graph, and simultaneously assign a value for an edge property map for each of the added edges. When I try the following example (with no edge property map information) works fine: elist1 = np.array([[0,1], [1, 2]]) #edge list with no value for edge property map data (only the source and target for the edges) g = gt.Graph() my_eprop = g.new_edge_property('bool') g.add_edge_list(elist) But with the following example, I get the error below: elist2 = np.array([[0,1, 1], [1, 2, 0]]) #edge list with edge property map value in 3rd column g = gt.Graph() my_eprop = g.new_edge_property('bool') g.add_edge_list(elist2, eprops=my_eprop) Error: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-48-6a949ea52c9b> in <module>() ----> 1 g.add_edge_list(elist2, eprops=my_eprop) /usr/lib/python2.7/dist-packages/graph_tool/__init__.pyc in add_edge_list(self, edge_list, hashed, string_vals, eprops) 1969 eprops = () 1970 else: -> 1971 convert = [_converter(x.value_type()) for x in eprops] 1972 eprops = [_prop("e", self, x) for x in eprops] 1973 if not isinstance(edge_list, numpy.ndarray): /usr/lib/python2.7/dist-packages/graph_tool/__init__.pyc in __getitem__(self, k) 534 kt = "Graph" 535 raise ValueError("invalid key '%s' of type '%s', wanted type: %s" --> 536 % (str(k), str(type(k)), kt) ) 537 538 def __setitem__(self, k, v): ValueError: invalid key '0' of type '<type 'int'>', wanted type: Edge --------------------------------------------------------------------------- Please help! -- 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.

4 9

from graph_tool.all import * gives ImportError
by vineesh vs 21 Jun '18

21 Jun '18

I tried the following command in Python and ipython prompt (2.7 version) --------------------------------------------------------------------------- In [1]: from graph_tool.all import * --------------------------------------------------------------------------- Its giving the following error. How can we solve it? --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-1-100bbe2bc9d9> in <module>() ----> 1 from graph_tool.all import * /usr/lib/python2.7/dist-packages/graph_tool/__init__.py in <module>() 103 104 from .dl_import import * --> 105 dl_import("from . import libgraph_tool_core as libcore") 106 __version__ = libcore.mod_info().version 107 /usr/lib/python2.7/dist-packages/graph_tool/dl_import.pyc in dl_import(import_expr) 55 56 try: ---> 57 exec(import_expr, local_dict, global_dict) 58 finally: 59 sys.setdlopenflags(orig_dlopen_flags) # reset it to normal case to <string> in <module>() ImportError: /usr/lib/python2.7/dist-packages/graph_tool/libgraph_tool_core.so: undefined symbol: _ZN5boost9iostreams4zlib6finishE --------------------------------------------------------------------------- Thanks

4 5

get 500 error when I try to pull graph tool from git repo
by Reckoner 18 Jul '17

18 Jul '17

am I the only one getting this error? Thanks!

5 6

Question regarding layered SBM
by treinz 04 Apr '17

04 Apr '17

Hi all, I'm new to the graph theory field and graph-tool package. Can anyone help me with the following questions on SBM of layered graph: 1) In the example shown in https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge…, the edge covariates for the Les Misérables network is passed via g.ep.value: state = gt.minimize_blockmodel_dl(g, deg_corr=False, layers=True, state_args=dict(ec=g.ep.value, layers=False)) In this case, does the constructed layered model automatically detect how many layers there should be in order to obtain a best fit SBM? If so, how can one retrieve the layer membership of each edge? If not, is there a way to do so in graph-tool via other function calls? 2) There's a so called 'independent layers' model discussed in the reference: Peixoto, T. P., Phys. Rev. E, 2015, 92, 042807 and it seems that setting state_args=dict(ec=g.ep.value, layers=True) in the example should use this model instead of the edge covariate model. But it seems from the paper that on is required to input the number of layers ('C' as in Fig. 3 of the reference). So how exactly should I use graph-tool to use the 'independent layers' model? Or is the algorithm capable of automatically detecting 'C' or the number of layers from the data? Thanks, Tim

3 11

number of hierarchy levels running off to infinity
by Peter Straka 27 Mar '17

27 Mar '17

Dear list, I'm trying to fit a nested blockmodel to a (bipartite) network with ~10^6 edges. The algorithm minimize_nested_blockmodel_dl() doesn't terminate; it keeps on adding layers indefinitely until it runs out of memory / hits walltime, see below. What could be going on? Many thanks in advance, Peter level 2 : replaced (94, 1) -> (94, 16) , dS: -15452.0134871 3 l=3 B: 8 <- 16 shrinking 16 -> 11 l=3 B: 8 <- 16 B=11 niter: 1 count: 1 breaks: 1 min_S: 1002.5742 max_S: 1002.5742 S: 1002.5742 ΔS: 0.00000 moves: 0 l=3 B: 8 <- 16 shrinking 11 -> 8 l=3 B: 8 <- 16 B=8 niter: 1 count: 1 breaks: 1 min_S: 838.70438 max_S: 838.70438 S: 838.70438 ΔS: 0.00000 moves: 0 l=3 Current bracket: (2, 8, 16) (691.21840695494018, 838.70438205706921, 1340.6798309766764) l=3 B: 5 <- 8 shrinking 8 -> 5 l=3 B: 5 <- 8 B=5 niter: 1 count: 0 breaks: 0 min_S: 752.39171 max_S: 753.34067 S: 752.39171 ΔS: -0.948956 moves: 2 l=3 B: 5 <- 8 B=5 niter: 2 count: 0 breaks: 0 min_S: 751.86303 max_S: 753.34067 S: 751.86303 ΔS: -0.528683 moves: 1 l=3 B: 5 <- 8 B=5 niter: 3 count: 1 breaks: 1 min_S: 751.86303 max_S: 753.34067 S: 751.86303 ΔS: 0.00000 moves: 0 l=3 Current bracket: (2, 5, 8) (691.21840695494018, 751.86302859686145, 838.70438205706921) l=3 B: 3 <- 5 shrinking 5 -> 3 l=3 B: 3 <- 5 B=3 niter: 1 count: 0 breaks: 0 min_S: 720.12311 max_S: 720.62000 S: 720.12311 ΔS: -0.496886 moves: 1 l=3 B: 3 <- 5 B=3 niter: 2 count: 1 breaks: 1 min_S: 720.12311 max_S: 720.62000 S: 720.12311 ΔS: 0.00000 moves: 0 l=3 Current bracket: (2, 3, 5) (691.21840695494018, 720.123114036723, 751.86302859686145) l=3 Current bracket: (2, 2, 3) (691.21840695494018, 691.21840695494018, 720.123114036723) l=3 Bisect at B = 2 with S = 691.2184069549402 l=3 Best result: B = 2, S = 691.2184069549402 level 3 : replaced (16, 1) -> (16, 2) , dS: -628.252218219 4 l=4 Current bracket: (2, 2, 2) (26.653307346627116, 26.653307346627116, 26.653307346627116) l=4 Current bracket: (2, 2, 2) (26.653307346627116, 26.653307346627116, 26.653307346627116) l=4 Bisect at B = 2 with S = 26.65330734662712 l=4 Best result: B = 2, S = 26.65330734662712 level 4 : replaced (2, 1) -> (2, 2) , dS: -1.86264514923e-09 5 l=5 Current bracket: (2, 2, 2) (26.653307346627116, 26.653307346627116, 26.653307346627116) l=5 Current bracket: (2, 2, 2) (26.653307346627116, 26.653307346627116, 26.653307346627116) l=5 Bisect at B = 2 with S = 26.65330734662712 l=5 Best result: B = 2, S = 26.65330734662712 level 5 : replaced (2, 1) -> (2, 2) , dS: -1.86264514923e-09 6 l=6 Current bracket: (2, 2, 2) (26.653307346627116, 26.653307346627116, 26.653307346627116) l=6 Current bracket: (2, 2, 2) (26.653307346627116, 26.653307346627116, 26.653307346627116) l=6 Bisect at B = 2 with S = 26.65330734662712 l=6 Best result: B = 2, S = 26.65330734662712 level 6 : replaced (2, 1) -> (2, 2) , dS: -1.86264514923e-09 7 l=7 Current bracket: (2, 2, 2) (26.653307346627116, 26.653307346627116, 26.653307346627116) l=7 Current bracket: (2, 2, 2) (26.653307346627116, 26.653307346627116, 26.653307346627116) l=7 Bisect at B = 2 with S = 26.65330734662712 l=7 Best result: B = 2, S = 26.65330734662712 level 7 : replaced (2, 1) -> (2, 2) , dS: -1.86264514923e-09 8 l=8 Current bracket: (2, 2, 2) (26.653307346627116, 26.653307346627116, 26.653307346627116) l=8 Current bracket: (2, 2, 2) (26.653307346627116, 26.653307346627116, 26.653307346627116) l=8 Bisect at B = 2 with S = 26.65330734662712 and l is increasing into the thousands if the left alone.

2 8

Use of edge's weights in model inference
by Andrea Briega 15 Mar '17

15 Mar '17

Dear Dr. Tiago Peixoto, I have a question regarding weighted graphs. I would like to classify a network with weighted edges into groups taking into account these weights. I have used minimize_blockmodel_dl and minimize_nested_blockmodel_dl before but I don't know exactly how to implement these functions in a weighted network. I'm not sure if I have to use the layered version, but in this case I don't know how to get these layers from my network. Thank you very much and sorry for the basic question, Andrea

3 5

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

graph-tool February 2017