Identifying influential spreaders in complex networks based on gravity formula

TL;DR

This paper introduces a gravity-based centrality measure for identifying influential spreaders in complex networks, outperforming traditional centrality measures in real and synthetic networks, as validated by epidemic modeling.

Contribution

The paper proposes a novel gravity centrality index that combines node mass and distance to better identify influential spreaders in networks.

Findings

Gravity centrality outperforms traditional measures in real networks.

The method effectively identifies influential spreaders in synthetic networks.

Validated by SIR epidemic model simulations.

Abstract

How to identify the influential spreaders in social networks is crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases and rumors, and so on. In this paper, by viewing the k-shell value of each node as its mass and the shortest path distance between two nodes as their distance, then inspired by the idea of the gravity formula, we propose a gravity centrality index to identify the influential spreaders in complex networks. The comparison between the gravity centrality index and some well-known centralities, such as degree centrality, betweenness centrality, closeness centrality, and k-shell centrality, and so forth, indicates that our method can effectively identify the influential spreaders in real networks as well as synthetic networks. We also use the classical Susceptible-Infected-Recovered (SIR) epidemic model to verify the good performance of our method.