Amazon Product Dataset Community Detection Metrics and Algorithms

Abstract

Community detection in social network analysis is crucial for understanding network structure and organization. It helps identify cohesive groups of nodes, allowing for targeted analysis and interventions. Girvan-Newman, Walktrap, and Louvain are popular algorithms used for community detection. Girvan-Newman focuses on betweenness centrality, Walktrap uses random walks, and Louvain optimizes modularity. Experimental results show that the label propagation algorithm (LPA) is efficient in extracting community structures. LPA has linear time complexity and does not require prior specification of the number of communities. However, it focuses on characterizing the number of communities rather than labeling them. K-clique performs well when the number of communities is known in advance. Louvain excels in modularity and community identification. Overall, community detection algorithms are essential for understanding network structures and functional units.