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 positive.
I would expect differences in the values but not by so far (+ shows modularity, - shows anti-modularity).
Any idea why this happens?
My code and data (https://gofile.io/?c=h24mcU):
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 (Brain Connectivity Toolbox)
using community_louvain.m: Louvain community detection algorithm
clear all load('A.mat') [M,Q]=community_louvain(A); Q = 0.1466
PYTHON version of community_louvain.m: https://github.com/aestrivex/bctpy/blob/f9526a693a9af57051762442d8490dcdf2ebf4e3/bct/algorithms/modularity.py#L71,
import bct split, Q = bct.community_louvain(A) Q 0.14659657544165258
again I get approx. 0.1466 that matches the Matlab and Python BCT-based results but is far from the