I would recommend posting the question on https://igraph.discourse.group/ as
that forum is meant to replace this mailing list, and provides a nicer
discussion environment.

On Sun, 29 Mar 2020 at 15:08, serafim loukas <seral...@hotmail.com> wrote:

> Just to add that I just found the *python* version of BCT (the Matlab
> toolbox that I used to benchmark igraph).
>
> Using this:
> https://github.com/aestrivex/bctpy/blob/f9526a693a9af57051762442d8490dcdf2ebf4e3/bct/algorithms/modularity.py#L71,
> again I get approx. 0.1466 that matches the Matlab based results but is far
> from the python output (Q).
>
> Makis
>
> On 29 Mar 2020, at 14:56, serafim loukas <seral...@hotmail.com> wrote:
>
> Hi igraph community,
>
>
> I have a graph and I want to estimate the modularity (Q) based on the
> Louvain split of the graph.
>
> In python I used igraph and to compare, I also estimated Q in Matlab.
> Based on igraph, the Q is negative (weird) whereas the Q based on the
> Matlab estimation is possitive.
>
> I would expect differences in the values but not by so far (+ shows
> modularity, - shows anti-modularity).
> Any idea why this happens?
>
> Makis
>
> ———————————
>
> My code and data:
>
>
> PYTHON
>
> ```
> import numpy as np
> import scipy.io
> from igraph import *
>
> A = scipy.io.loadmat('A.mat')['A']
>
> graph = Graph.Weighted_Adjacency(A.tolist(), mode=ADJ_UNDIRECTED,
> attr="weight", loops=False)
> Louvain = graph.community_multilevel(weights=graph.es['weight'],
> return_levels=False)
> Q = graph.modularity(Louvain)
> print(Q)
>
> -0.001847596203445795
> ```
>
>
> MATLAB (https://sites.google.com/site/bctnet/measures/list)
> using community_louvain.m: Louvain community detection algorithm
>
> ```
> clear all
> load('A.mat')
> [M,Q]=community_louvain(A);
>
> Q =
>
>    0.1466
> ```
>
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