# Group based Centrality for Immunization of Complex Networks

## TL;DR

This paper introduces a novel group-based centrality method using Shapley value to identify key nodes for immunization in complex networks, effectively controlling epidemic and misinformation spread.

## Contribution

It proposes a game-theoretic approach with Shapley value to select influential nodes for network immunization, enhancing existing strategies.

## Key findings

- Effective identification of key nodes for immunization.
- Improved containment of contagion outbreaks.
- Validated on empirical and model networks.

## Abstract

Network immunization is an extensively recognized issue in several domains like virtual network security, public health and social media, to deal with the problem of node inoculation so as to minimize the transmission through the links existed in these networks. We aim to identify top ranked nodes to immunize networks, leading to control the outbreak of epidemics or misinformation. We consider group based centrality and define a heuristic objective criteria to establish the target of key nodes finding in network which if immunized result in essential network vulnerability. We propose a group based game theoretic payoff division approach, by employing Shapley value to assign the surplus acquired by participating nodes in different groups through the positional power and functional influence over other nodes. We tag these key nodes as Shapley Value based Information Delimiters (SVID). Experiments on empirical data sets and model networks establish the efficacy of our proposed approach and acknowledge performance of node inoculation to delimit contagion outbreak.