A Parallel Particle Swarm Optimization for Community Detection in Large Attributed Graphs

Abstract

Social network analysis (SNA) is an active research domain that mainly deals with large social graphs and their properties. Community detection (CD) is one of the active research topics belonging to this domain. Social graphs in real-time are huge, complex, and require more computational resources to process. In this paper, the authors present a CPU-based hybrid parallelization architecture that combines both master-slave and island models. They use particle swarm optimization (PSO)-based clustering approach, which models community detection as an optimization problem and finds communities based on concepts of PSO. The proposed model is scalable, suitable for large datasets, and is tested on real-time social networking datasets with node attributes belonging to all three sizes (small, medium, and large). The model is tested on standard benchmark functions and evaluated on well-known evaluation strategies related to both community clusters and parallel systems to show its efficiency.