[graph-tool] Getting edge probabilities for SBM.

Hi Tiago, I am a new user for Graph Tool. I was trying to do a fitting approach for a graph. And to measure the goodness of fit, I intend to use log of likelihood score for the generated graph and the real graph. For this, I needed to calculate the probabilities of each edge existing in the gener

Re: [graph-tool] Getting edge probabilities for SBM.

Hi Tiago, how do we obtain the normalisation constant in this case? Please help with some sample code for it. Regards, Sukrit -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/Getting-edge-probabilities-for-SBM-tp4026758p4026760.html S

Re: [graph-tool] Getting edge probabilities for SBM.

Please verify if I have understood this correctly: I calculate a normalisation constant for each iteration of the network generated by 'mcmc_equilibrate'. This constant is the sum of probabilities of each edge existing between all pairs of nodes in the graph. To get the actual value of an edge ex

Re: [graph-tool] Getting edge probabilities for SBM.

Hi Tiago, so this is what I want: I want to calculate the log likelihood that the SBM modeled from a network fits the network correctly. I want to compare this with the likelihood of other model fits to the network. I have the log likelihood scores for other model fits, but SBM is pending. Till now

[graph-tool] Getting out degree for each of the nodes of a BlockState

Hi Tiago, I wanted to create a probability distribution function for the inferred network calculated by SBM (using function 'minimise_blockmodel_dl') from the original network. The object in consideration in this case is the 'BlockState' object. I couldn't find a way to do this, since there is no

[graph-tool] Takes enormous memory and then eventually gets terminated.

Hi, I have a graph with around 9500 nodes and 37000 edges. I want to determine the SBM corresponding to this network, but when I run minimize_blockmodel_dl function on it, I get a 'killed:9' error. I checked the memory consumption and it exceeded 50 GB at one point. Is this expected? Or is there

Re: [graph-tool] Takes enormous memory and then eventually gets terminated.

1. graph_tool version: 2.22 (commit 44bf2b92, Thu Mar 2 23:08:39 2017 +) 2. How do I put the graph over here? 3. Here: import numpy as np from graph_tool.all import * f_network = np.genfromtxt("Name of Edge List File", delimiter=',') f_network = f_network.astype(int) for edge in f_network:

Re: [graph-tool] Takes enormous memory and then eventually gets terminated.

binary_edgelist_hprd.csv sbm_ppi.py Uploaded both the CSV and python cod

Re: [graph-tool] Takes enormous memory and then eventually gets terminated.

Also, I created the gt file for the network, but it is around 800 MB! So, cannot upload it. -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/Takes-enormous-memory-and-then-eventually-gets-terminated-tp4027236p4027241.html Sent from the

[graph-tool] Dropping constants while calculating Description Length/Log Likelihood?

Hi Tiago, I wanted to enquire whether the DL that is returned using entropy() function drops any constant factors during calculations. In the calculations to be found in most papers on this topic, there are many factors omitted from the expressions for likelihood, because they are independent of t

[graph-tool] Displaying Overlapping Communities for Networks Without Clustering with SBMs

Hi, I am trying to generate plots for communities detected by another algorithm (outside graph-tool). The communities are overlapping and I wanted to display something like nodes with multiple colors (like we have for SBMs). For this I set a new vertex property and assign nodal memberships to it. I

Re: [graph-tool] Displaying Overlapping Communities for Networks Without Clustering with SBMs

This should reproduce the error: g = Graph() v1 = g.add_vertex() v2 = g.add_vertex() e = g.add_edge(v1, v2) pv1 = g.new_vertex_property("vector"); pv1.set_2d_array(np.zeros((2, 2))) graph_draw(g, vertex_shape="pie", vertex_pie_fractions=pv1, edge_gradient=None, output="lesmis-sbm-marginals.svg")

[graph-tool] Getting negative entropy/positive Log likelihood for a weighted network with positive edge weights.

Dear Tiago, I am trying to model a weighted dense network with edge weights in the range 0 to 1, with the non-hierarchical SBM for both degree corrected and non-degree corrected versions. However, I get negative entropy for both dc and ndc SBMs. I have attached a sample network for your considerati