• Aucun résultat trouvé

Evolutionary algorithm and modularity for detecting communities in networks

N/A
N/A
Info
Télécharger
Protected

Academic year: 2021

Partager "Evolutionary algorithm and modularity for detecting communities in networks"

Copied!
1
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

(1)

Evolutionary

algorithm

and

modularity

for

detecting

communities in networks

Authors

Saoud Bilal, Moussaoui Abdelouahab

Publication date

2017/5/1

Journal

Physica A: Statistical Mechanics and its Applications

Volume 473 Pages 89-96 Publisher North-Holland Description

Evolutionary algorithms are very used today to resolve problems in many fields. There are few community detection methods in networks based on evolutionary algorithms. In our paper, we develop a new approach of community detection in networks based on evolutionary algorithm. In this approach we use an evolutionary algorithm to find the first community structure that maximizes the modularity. After that we improve the community structure through merging communities to find the final community structure that has the high value of modularity. We provide a general framework for implementing our approach. Compared with the state of art algorithms, simulation results on computer-generated and real world networks reflect the effectiveness of our approach.

Références

Télécharger maintenant ( PDF - 1 Page - 143.60 KB )

Documents relatifs

Models and Algorithms for the Design of Service Overlay Networks

We provide numerical results for a set of realistic-size instances and investigate the impact of different parameters on the SON design problem, such as number and installation cost

Supervised and Evolutionary Learning of Echo State Networks

Note however that with smaller reservoir sizes (e.g. N = 30), optimizing the reser- voir neuron slopes (Slopes variant) yielded even better fitness than the standard procedure

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

MACHINE LEARNING FOR COMMUNICATIONS ML is conventionally thought to have its application justified in the situations, where there is no exact mathematical model of the system

Bayesian Networks: A State-Of-The-Art Survey

The articles are classified based on a scheme that consists of three main Bayesian Networks topics: Bayesian Networks Structure Learning, Advanced Application of Bayesian

Detecting sources of computer viruses in networks: Theory and experiment

We see that rumor centrality and distance centrality have similar performance, but we see that for the power grid net- work, rumor centrality is better able to correctly find the

Monocular Odometry for Underwater Vehicles with Online Estimation of the Scale Factor

Monocular Odometry for Underwater Vehicles with Online Estimation of the Scale Factor..

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Department of Bioinformatics & Systems Biology, Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, and the

Detecting the Collapse of Cooperation in Evolving Networks

The dif- ference between the two classes is that non-structural indicators reflect changes in the numbers of cheaters and cooperators (demographic changes), whereas