An efficient Genetic Algorithm with Modified Crossover Operator for Community Detection in Social Networks

Benazi, Makhlouf; Chaabane, Lamiche

doi:10.1109/isia51297.2020.9416556

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…The hierarchical algorithms can be divided into divisive methods 12,13 and aggregation methods 14,15 Approximation methods which optimize the value of a quality function, generally, the function is the modularity, 16 such in greedy algorithm, 14 genetic algorithm, 17, 18 particle swarm optimization, 19 and simulated annealing 20 Methods based on the label propagation algorithm (LPA) 21 is another family of algorithms that recently attracted a lot of attention due to their simplicity and low complexity, such as References 22‐25.…”

Section: Introductionmentioning

confidence: 99%

A complex network community detection algorithm based on random walk and label propagation

Benazi¹,

Lounnas

Mokhtari³

2022

Trans Emerging Tel Tech

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The community structure is proving to have a very important role in the understanding of complex networks, but discovering them remains a very difficult problem despite the existence of several methods. In this article, we propose a novel algorithm for discovering communities in complex networks based on a modified random walk (RW) and label propagation algorithm (LPA). First, we calculate the similarity between nodes based on the new formula of RW. Then, the labels are propagated by the obtained similarity of the first step using LPA. Finally, the third step will be a new measure to find the optimal partitioning of communities. Experimental results obtained on several real and synthetic networks reveal that our algorithm outperforms existing methods in finding communities.

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