Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/23964
Title: Attributed-Based Label Propagation Method for Balanced Modularity and Homogeneity Community Detection
Authors: Moosa, J
Awad, W
Kalganova, T
Keywords: community detection;label propagation;homogeneity;COVID-19;modularity
Issue Date: 3-Feb-2022
Publisher: SCITEPRESS
Citation: Moosa, J., Awad, W. and Kalganova, T. (2022) 'Attributed-based Label Propagation Method for Balanced Modularity and Homogeneity Community Detection', Proceedings of the 14th International Conference on Agents and Artificial Intelligence, Volume 3: ICAART, Online, 3-5 February, pp. 905 - 912. doi: 10.5220/0010928200003116.
Abstract: Community Detection is an expanding field of interest in many scopes, e.g., social science, bibliometrics, marketing and recommendations, biology etc. Various community detection tools and methods have been proposed in the last years. This research is to develop an improved Label Propagation algorithm (Attribute-Based Label Propagation ABLP) that considers the nodes’ attributes to achieve a fair Homogeneity value, while maintaining high Modularity measure. It also formulates an adaptive Homogeneity measure, with penalty and weight modulation, that can be utilized in consonance with the user’s requirements. Based on the literature review, a research gap of employing Homogeneity in Community Detection was identified, and accordingly, Homogeneity as a constraint in Modularity based methods is investigated. In addition, a novel dataset constructed on COVID-19 contact tracing in the Kingdom of Bahrain is proposed, to help identify communities of infected persons and study their attributes ’ values. The implementation of proposed algorithm performed high Modularity and Homogeneity measures compared with other algorithms.
URI: https://bura.brunel.ac.uk/handle/2438/23964
DOI: https://doi.org/10.5220/0010928200003116
metadata.dc.relation.isreplacedby: 2438/29254
http://bura.brunel.ac.uk/handle/2438/29254
ISBN: 978-989-758-547-0 (PDF)
ISSN: 2184-3589
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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