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Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends

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Abstract

Over the past couple of decades, the research area of network community detection has seen substantial growth in popularity, leading to a wide range of researches in the literature. Nature-inspired optimization algorithms (NIAs) have given a significant contribution to solving the community detection problem by transcending the limitations of other techniques. However, due to the importance of the topic and its prominence in many applications, the information on it is scattered in various journals, conference proceedings, and patents, and lacked a focused-literature that synthesizes them in a single document. This review aims to provide an overview of the NIAs and their role in solving community detection problems. To achieve this goal, a systematic study is performed on NIAs, followed by historical and statistical analysis of the researches involved. This would lead to the identification of future trends, as well as the discovery of related research challenges. This review provides a guide for researchers to identify new areas of research, as well as directing their future interest towards developing more effective frameworks in the context of nature-inspired community detection algorithms.

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Abbreviations

ACO:

Ant colony optimization

BA:

Bat algorithm

BCO:

Bee colony optimization

CA:

Cultural algorithm

CD:

Community detection

CS:

Cuckoo search

DE:

Differential evolution

EA:

Evolutionary algorithm

FA:

Firefly algorithm

GA:

Genetic algorithm

HO:

Heuristic operator

LDA:

Latent Dirichlet allocation

MA:

Memetic algorithm

MH:

Metaheuristic

MOO:

Multi-objective optimization

MPM:

Marginal product model

NIA:

Nature-inspired algorithm

NP:

Non-deterministic polynomial time problems

PPI:

Protein-protein interaction

SA:

Simulated annealing

SN:

Social network

SOO:

Single objective optimization

SSN:

Signed social network

X:

Crossover

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  1. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310, UTM Skudai, Johor, Malaysia

    Dhuha Abdulhadi Abduljabbar, Siti Zaiton Mohd Hashim & Roselina Sallehuddin

  2. Department of Computer Science, Baghdad University, Baghdad, Iraq

    Dhuha Abdulhadi Abduljabbar

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Abduljabbar, D.A., Hashim, S.Z.M. & Sallehuddin, R. Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends. Telecommun Syst 74, 225–252 (2020). https://doi.org/10.1007/s11235-019-00636-x

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