A1322
Title: On a notion of graph centrality based on $L_1$ data depth
Authors: Seungwoo Kang - Seoul National University (Korea, South) [presenting]
Hee-Seok Oh - Seoul National University (Korea, South)
Abstract: A new measure to assess the centrality of vertices in an undirected and connected graph is proposed. The proposed measure, $L_1$ centrality, can adequately handle graphs with weights assigned to vertices and edges. The aim is to provide tools for graphical and multiscale analysis based on the $L_1$ centrality. Specifically, the suggested analysis tools include the target plot, $L_1$ centrality-based neighborhood, and local $L_1$ centrality. Most importantly, the focus is closely associated with the concept of data depth for multivariate data, which allows for a wide range of practical applications of the proposed measure. The tools are demonstrated with two interesting examples: the Marvel Cinematic Universe movie network and the bill co-sponsorship network of the 21st National Assembly of South Korea.
