Attributed-based Label Propagation Method for Balanced Modularity and Homogeneity Community Detection
14th International Conference on Agents and Artificial Intelligence (ICAART)
; : 905-912, 2022.
Article
in English
| Web of Science | ID: covidwho-1798803
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.
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
14th International Conference on Agents and Artificial Intelligence (ICAART)
Year:
2022
Document Type:
Article
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