BSODCS: Bee Swarm Optimization for Detecting Community Structure

Narimene Dakiche

2023

Abstract

This paper presents, BSODCS, a Bee Swarm Optimization for detecting community structure within networks. It employs artificial bees to explore a search space and construct solutions for community detection. To accommodate the specific features of networks, we adopt a locus-based adjacency encoding scheme. Each bee makes decisions regarding its neighboring solutions and shares information through a dance. To explore the neighborhood of each bee, we use Pearson’s correlation as the heuristic information. The modularity of the bees’ solutions serves as a metric for evaluating their quality. The algorithm is tested on well-known real-world networks, and the experimental findings demonstrate that BSODCS outperforms other existing swarm-based methods, delivering higher-quality results.

Paper Citation

in Harvard Style

Dakiche N. (2023). BSODCS: Bee Swarm Optimization for Detecting Community Structure. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-672-9, SciTePress, pages 422-428. DOI: 10.5220/0012209300003584

in Bibtex Style

@conference{webist23,
author={Narimene Dakiche},
title={BSODCS: Bee Swarm Optimization for Detecting Community Structure},
booktitle={Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2023},
pages={422-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012209300003584},
isbn={978-989-758-672-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - BSODCS: Bee Swarm Optimization for Detecting Community Structure
SN - 978-989-758-672-9
AU - Dakiche N.
PY - 2023
SP - 422
EP - 428
DO - 10.5220/0012209300003584
PB - SciTePress