2019/05/08 by Orazio Giustolisi, Luca Ridolfi, Giustolisi, Orazio +3
Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social Capital and Networks #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1905.03300
openalex publication_date 2019/05/08 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
Complex network theory (CNT) is gaining a lot of attention in the scientific\ncommunity, due to its capability to model and interpret an impressive number of\nnatural and anthropic phenomena. One of the most active CNT field concerns the\nevaluation of the centrality of vertices and edges in the network. Several\nmetrics have been proposed, but all of them share a topological point of view,\nnamely centrality descends from the local or global connectivity structure of\nthe network. However, vertices can exhibit their own intrinsic relevance\nindependent from topology; e.g., vertices representing strategic locations\n(e.g., hospitals, water and energy sources, etc.) or institutional roles (e.g.,\npresidents, agencies, etc.). In these cases, the connectivity network structure\nand vertex intrinsic relevance mutually concur to define the centrality of\nvertices and edges. The purpose of this work is to embed the information about\nthe intrinsic relevance of vertices into CNT tools to enhance the network\nanalysis. We focus on the degree, closeness and betweenness metrics, being\namong the most used. Two examples, concerning a social (the historical Florence\nfamily marriage network) and an infrastructure (a water supply system) network,\ndemonstrate the effectiveness of the proposed relevance-embedding extension of\nthe centrality metrics.\n