# Locating influential nodes via dynamics-sensitive centrality

arXiv: https://arxiv.org/abs/1504.06672 · HTML: https://mlsanity.com/paper/1504.06672/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2015.

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

- [Predicting Critical Nodes in Temporal Networks by Dynamic Graph Convolutional Networks](https://mlsanity.com/paper/2106.10419/) (2021)
- [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)
- [Opinion Leader Detection in Online Social Networks Based on Output and Input Links](https://mlsanity.com/paper/2208.13161/) (2022)

## Abstract (arXiv)

With great theoretical and practical significance, locating influential nodes of complex networks is a promising issues. In this paper, we propose a dynamics-sensitive (DS) centrality that integrates topological features and dynamical properties. The DS centrality can be directly applied in locating influential spreaders. According to the empirical results on four real networks for both susceptible-infected-recovered (SIR) and susceptible-infected (SI) spreading models, the DS centrality is much more accurate than degree, k-shell index and eigenvector centrality.