Enhanced Guided Local Search for Addressing the Graph Burning Problem
Lamia Sadeg-Belkacem, Imad Tamelghaghet, Fatima Benbouzid-Si Tayeb
2025
Abstract
Information spread is crucial in network science, investigating how influence, data, or contagion propagates through networks. Graph burning offers a simplified deterministic model for addressing the NP-complete Graph Burning Problem. Acknowledging the unique characteristics of this problem, this paper introduces an efficient guided local search approach, leveraging betweenness centrality to initialize the solution process and integrating an augmented function with penalty terms to optimize the burning sequence. Using a binary search mechanism, candidate values are iteratively tested. Experimental results on 15 benchmark graphs demonstrate the algorithm’s superior performance compared to state-of-the-art methods.
DownloadPaper Citation
in Harvard Style
Sadeg-Belkacem L., Tamelghaghet I. and Tayeb F. (2025). Enhanced Guided Local Search for Addressing the Graph Burning Problem. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 758-765. DOI: 10.5220/0013179500003890
in Bibtex Style
@conference{icaart25,
author={Lamia Sadeg-Belkacem and Imad Tamelghaghet and Fatima Tayeb},
title={Enhanced Guided Local Search for Addressing the Graph Burning Problem},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={758-765},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013179500003890},
isbn={978-989-758-737-5},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Enhanced Guided Local Search for Addressing the Graph Burning Problem
SN - 978-989-758-737-5
AU - Sadeg-Belkacem L.
AU - Tamelghaghet I.
AU - Tayeb F.
PY - 2025
SP - 758
EP - 765
DO - 10.5220/0013179500003890
PB - SciTePress