Run modularity density-based clustering
Project description
Community detection by fine-tuned optimization of modularity and modularity density
Dependencies
Python | >= 3.5.0 |
NetworkX | >= 2.2 |
NumPy | >= 1.15.1 |
SciPy | >= 1.1.0 |
Installation
pip install -i https://test.pypi.org/simple/ 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, normalize=False, seed=100)
print(community_array)
>> [1 1 1 1 3 3 3 1 6 2 3 5 1 1 6 6 3 1 6 1 6 1 6 6 4 4 6 4 4 6 6 4 6 6]
computed_metric = modularity_density(adj, community_array, np.unique(community_array))
print(computed_metric)
>> 0.23821951467671487
Description
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: https://github.com/ckmanalytix/modularity-density/
References
[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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