Distinctiveness centrality in social networks

PLoS One. 2020 May 22;15(5):e0233276. doi: 10.1371/journal.pone.0233276. eCollection 2020.

Abstract

The determination of node centrality is a fundamental topic in social network studies. As an addition to established metrics, which identify central nodes based on their brokerage power, the number and weight of their connections, and the ability to quickly reach all other nodes, we introduce five new measures of Distinctiveness Centrality. These new metrics attribute a higher score to nodes keeping a connection with the network periphery. They penalize links to highly-connected nodes and serve the identification of social actors with more distinctive network ties. We discuss some possible applications and properties of these newly introduced metrics, such as their upper and lower bounds. Distinctiveness centrality provides a viewpoint of centrality alternative to that of established metrics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computer Simulation
  • Humans
  • Models, Theoretical*
  • Social Networking*

Grants and funding

M.N.: Work partially supported by MIUR, the Italian Ministry of Education, University and Research, under PRIN Project n. 20174LF3T8 AHeAD (Efficient Algorithms for HArnessing Networked Data). A.F.C.: Work partially supported by the University of Perugia, through the program "Fondo Ricerca di Base 2019", project n. RICBA19LTI ("Business Intelligence, Data Analytics e simulazioni a eventi discreti per le Smart Companies nell’era dell’Industria 4.0"). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.