# Identifying influential spreaders in complex networks based on gravity formula

arXiv: https://arxiv.org/abs/1505.02476 · HTML: https://mlsanity.com/paper/1505.02476/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2015.

## Built on by (most cited first, 4 of 4)

- [Critical Nodes Identification in Complex Networks: A Survey](https://mlsanity.com/paper/2507.06164/) (2025)
- [Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach](https://mlsanity.com/paper/2202.06229/) (2022)
- [A Network Science perspective of Graph Convolutional Networks: A survey](https://mlsanity.com/paper/2301.04824/) (2023)
- [XFlow: Benchmarking Flow Behaviors over Graphs](https://mlsanity.com/paper/2308.03819/) (2023)

## Abstract (arXiv)

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.