Attributed-based Label Propagation Method for Balanced Modularity and Homogeneity Community Detection

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.

Automated summary

Community Detection is a rapidly growing field with applications in various disciplines. This research aims to develop an improved Label Propagation algorithm that considers the attributes of nodes to achieve fair Homogeneity and high Modularity. It also introduces an adaptive Homogeneity measure and proposes a novel dataset for COVID-19 contact tracing. The proposed algorithm outperformed other algorithms in terms of Modularity and Homogeneity measures.