International Journal of Science, Engineering and TechnologyAuthors: Deepali Piple, Mukesh Sakle, Shaligram Prajapat
Abstract: Social Network Analysis relies upon community detection to identify groups of nodes within a network that maintain stronger inter-node relationships than their node linkages to all other components of the network. Community Detection enables researchers to understand how complex networks construct and establish their functional connections. This research tests various community detection techniques by way of several modularity-based community detection techniques using established benchmark social networks. The study evaluates four community detection algorithms: Label Propagation Algorithm (LPA) and Clauset-Newman-Moore (CNM) and Louvain and Leiden.Each of the four community detection algorithms was evaluated against five standard benchmark social networks including Karate, Dolphin, Football, Facebook, Polbooks, Les Misérables and Jazz.Performance evaluation metrics used for each of the four algorithms included the Modularity Index (MI), Conductance, Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI). Results from this research demonstrated that the Louvain method consistently produced higher MI values and had consistent performance across all of the various test data sets, while the LPA method performed all other methods in networks where community structures are clearly visible. The research also found that modularity-based optimizations successfully identified critical social network communities.
DOI: http://doi.org/10.5281/zenodo.20265823
