graph-tool April 2017

graph-tool@skewed.de

10 participants
18 discussions

Inference for the "karate network"
by Snehal Shekatkar 25 Apr '17

25 Apr '17

Dear all, Just as a test, I was trying to fit a degree-corrected SBM to the famous "karate-club" network. Almost everybody has "shown" that the network contains two communities. However, when I tried using in-built graph-tool algorithms, I can see only one: In [2]: G = gt.collection.data["karate"] In [3]: state = gt.minimize_blockmodel_dl(G, deg_corr=True) In [4]: state Out[4]: <BlockState object with 1 blocks (1 nonempty), degree-corrected, for graph <Graph object, undirected, with 34 vertices and 78 edges at 0xa288858c>, at 0xa288b9cc> I tried this repeatedly, but I always get only one group. Then I tried manually specifying 2 groups: In [11]: state = gt.minimize_blockmodel_dl(G, deg_corr=True, B_min=2) However, in this case, I get a strange partition as shown in the following figure: This is not even close to what many papers show. I appreciate that many algorithms consider noise as the real structure. However, even the famous paper by Karrer and Newman (Karrer, Brian, and Mark EJ Newman. "Stochastic blockmodels and community structure in networks." *Physical Review E* 83.1 (2011): 016107.) describes a very different division of the network. Am I missing something very fundamental here? Thank you Snehal Shekatkar -- Snehal M. Shekatkar Pune India

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Please participate in a research survey on graphs
by Siddhartha Sahu 21 Apr '17

21 Apr '17

Hi, My name is Siddhartha Sahu and I am a Master's student at University of Waterloo working on graph processing with Prof. Semih Salihoglu. As part of my research, I am running a survey on how graphs are used in the industry and academia. If you work on any kind of graph technology, please participate in the survey: *Survey link*: https://dsg-uwaterloo.typeform.com/to/EBihxG The survey will take about *5-10 minutes* to finish. Most of the questions are *multiple-choice questions*, and you can *skip any number of questions*. I would really appreciate if you filled out my survey :) Also, kindly forward the survey to any one else you know who also works on graph processing. Our goal is to help researchers who work on graph processing understand the types of data, computations, and software that are used in practice. We hope the information we can gather from participation in this survey can guide the research people like ourselves do in universities. We plan to share the information we gather from the survey with the wider research community through a publication. This survey has been reviewed and received ethics clearance through a University of Waterloo Research Ethics Committee. You will see that the first page of the survey contains a long consent page as required by the university. Thank you very much in advance for your support. Regards, Siddhartha Sahu s3sahu(a)uwaterloo.ca

1 0

draw specific levels of a hierarchy only
by sy 13 Apr '17

13 Apr '17

After obtaining a nested blockModel with state=minimize_blockmodel_dl(...), I can use draw_hierarchy(state...) to view the hierarchical block partition with a radial layout. However, the lowest level in the hierarchy has too many groups...is there a way to view the blocks at a higher level in the hierarchy? Thanks. -- 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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edge removal not removing value from property map
by Hobé Alex 12 Apr '17

12 Apr '17

Hi Guys I am trying to remove edges from my graph. I first copy my graph: gCopy = g.copy() pCopy = gCopy.new_edge_property("double") then I go through the edges in the original graph to check for a particular property: for j, e in enumerate(g.edges(): src = e.source() tgt = e.target() When a certain value for the property map of the original graph is found I use: if p[e] > value: print pCopy.a gCopy.remove_edge(e) print pCopy.a When I look at pCopy.a I see that the array does not shorten and that 0 values remain. These values do not show up when I use: for e in gCopy.edges(): print pCopy[e] I can still find these values, however, when I use for e in g.edges(): print pCopy[e] I want to use this array in the max flow algorithm and it complains: res = gt.edmonds_karp_max_flow(gCopy, src, tgt, pCopy) res.a = pCopy.a - res.a ValueError: operands could not be broadcast together with shapes (17,) (32,) Is there any way I can remove the zero values from pCopy.a? Thanks! Best, Alex

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Using pyinstaller with Graph-tool
by Evangelos Petsalis 07 Apr '17

07 Apr '17

Hi, I was wondering if anybody from the list has ever used pyinstaller on an python app that uses graph-tool? I am trying to create a self-contained file/directory that I can give to others without them having to go through the trouble of installing all the packages required for graph-tool. It seem that pyinstaller does not like graph-tool dependancies. It complains about importing all of graph-tools libraries (correlation, etc., etc.). Anybody has any experience with htis, or any suggestion how to go about doing something similar? Thanks -- 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.

2 1

Evaluating "goodness of fit" of stochastic block model
by P-M 06 Apr '17

06 Apr '17

Hello, I am trying to fit a stochastic block model to a network in order to ultimately do link prediction. I am reasonably happy comparing different versions of the SBMs to each other by computing and comparing posterior odds ratios so I can determine which configuration is best at describing my data. What I however am not sure about yet is how good the model is in actually describing the data (after all, if they are all very poor in fitting my particular network than the fact that one is better than the rest doesn't guarantee that it is actually any good at describing my data and thus my later predictions based on it will therefore also be off) and whether there is some metric or procedure I could use to get an idea for this. Does anybody have any advice on this? Thank you for your help in advance! With best wishes, Philipp -- 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.

2 1

Importing Data Set Collection - Segmentation fault
by P-M 04 Apr '17

04 Apr '17

Running graph-tool "2.23dev (commit b626e713, Tue Mar 28 22:29:08 2017 +0100)", compiled using GCC 5.4.0 on Ubuntu 16.04 I get a "Segmentation fault (core dumped)" error when running the following: import graph_tool.all as gt g = gt.collection.data["dolphins"] This issue does not seem to arise for me when running version "2.22 (commit 44bf2b92, Thu Mar 2 23:08:39 2017 +0000)" (i.e. the latest apt-get release). Is this an issue caused in the dev version of graph-tool or is this caused by me compiling something incorrectly? Can anybody reproduce this error? Best, Philipp -- 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.

2 7

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

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graph-tool April 2017