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Run modularity density-based clustering

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Community detection by fine-tuned optimization of modularity and modularity density


Python >= 3.5.0
NetworkX >= 2.2s
NumPy >= 1.15.1
SciPy >= 1.1.0


pip install modularitydensity

Quick Start

import networkx as nx
import numpy as np
from modularitydensity.metrics import modularity_density
from modularitydensity.fine_tuned_modularity_density import fine_tuned_clustering_qds

G = nx.karate_club_graph() #sample dataset
adj = nx.to_scipy_sparse_matrix(G) #convert to sparse matrix

community_array = fine_tuned_clustering_qds(G)
>> [2 2 2 2 4 4 4 2 3 3 4 2 2 2 3 3 4 2 3 2 3 2 3 3 1 1 3 3 3 3 3 1 3 3]

computed_metric = modularity_density(adj, community_array, np.unique(community_array))
>> 0.2312650016945721    


This package comprises two community detection algorithms which perform fine-tuned optimization of modularity and modularity density, respectively, of a community network structure. The fine-tuned algorithm iteratively carries out splitting and merging stages, alternatively, until neither splitting nor merging of the community structure improves the desired metric.

Also included are extensions of the fine_tuned optimizations of both modules. These extended versions account for any constraint on the maximum community size, while optimizing the desired metric.

Source code can be found at:


[1] CHEN M, KUZMIN K, SZYMANSKI BK. Community detection via maximization of modularity and its variants. IEEE Transactions on Computational Social Systems. 1(1), 46–65, 2014

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