Neighborhood-based Bridge Node Centrality Tuple for Preferential Vaccination of Nodes

TL;DR

This paper proposes using the Neighborhood-based Bridge Node Centrality (NBNC) tuple to identify key nodes for vaccination in networks, aiming to effectively reduce infection spread by targeting bridge nodes that connect different parts of the network.

Contribution

The study introduces the NBNC tuple as a novel centrality measure for vaccination strategies, demonstrating its effectiveness over degree centrality in epidemic control on real-world networks.

Findings

NBNC-based vaccination results in fewer infected nodes in simulations.

NBNC outperforms degree centrality in reducing infection spread.

Targeting bridge nodes effectively blocks infection pathways.

Abstract

We investigate the use of a recently proposed centrality tuple called the Neighborhood-based Bridge Node Centrality (NBNC) tuple to choose nodes for preferential vaccination so that such vaccinated nodes could provide herd immunity and reduce the spreading rate of infections in a complex real-world network. The NBNC tuple ranks nodes on the basis of the extent they play the role of bridge nodes in a network. A node is a bridge node, if when removed its neighbors are either disconnected or at least sparsely connected. We hypothesize that preferentially vaccinating such bridge nodes would block an infection to spread from a neighbor of the bridge node to an another neighbor that are otherwise not reachable to each other. We evaluate the effectiveness of using NBNC to reduce the spread of infections by conducting simulations of the spread of infections per the SIS (Susceptible-Infected-Susceptible) model on a collection of 10 complex real-world social networks. We observe the average fraction of infected nodes per round of the SIS simulations based on NBNC for preferential vaccination to be lower than that of the degree centrality-based preferential vaccination.