A comparison of centrality measures and their role in controlling the spread in epidemic networks

Abstract

The ranking of nodes in a network according to their centrality or ``importance'' is a classic problem that has attracted the interest of different scientific communities in the last decades. The COVID-19 pandemic has recently rejuvenated the interest in this problem, as the ranking may be used to decide who should be tested, or vaccinated, first, in a population of asymptomatic individuals. In this paper, we review classic methods for node ranking and compare their performance in a benchmark network that considers the community-based structure of society. The outcome of the ranking procedure is then used to decide which individuals should be tested, and possibly quarantined, first. Finally, we also review the extension of these ranking methods to weighted graphs and explore the importance of weights in a contact network by providing a toy model and comparing node rankings for this case in the context of disease spread.

Keywords:

• Network science
• graph theory
• centrality measures
• dynamic systems

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

J.M. acknowledges support of the OP RDE funded project CZ.02.1.01/0.0/0.0/16_019/0000765 ‘Research Center for Informatics’. S.Y. acknowledges support by TUBITAK (The Scientific and Technological Research Council of Turkey). S.K. acknowledges research support by NSERC Discovery Grant Grant RGPIN-2019-05408. J.B. acknowledges research support by NSERC Discovery Grant RGPIN-2021-03775. M.B., P.F., R.M.-S., T.P.& R.S. acknowledge support from EPSRC project EP/V018450/1. R.M.-S. acknowledges funding support from EPSRC grant EP/R018634/1. M.B. and T.P. also acknowledge funding support from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 739551 (KIOS CoE), E.C. & E.D acknowledge support by the Italian Ministry of Education, Universities and Research through the Research Project PRIN 2017 [grant no 2017CWMF93]‘Advanced Network Control of Future Smart Grids’ (project VECTORS).