# Identifying a set of influential spreaders in complex networks

arXiv: https://arxiv.org/abs/1602.00070 · HTML: https://mlsanity.com/paper/1602.00070/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2016.

## Built on by (most cited first, 10 of 10)

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- [Critical Nodes Identification in Complex Networks: A Survey](https://mlsanity.com/paper/2507.06164/) (2025)
- [Identifying Key Nodes for the Influence Spread using a Machine Learning Approach](https://mlsanity.com/paper/2412.01949/) (2024)
- [PINE: Pipeline for Important Node Exploration in Attributed Networks](https://mlsanity.com/paper/2512.07244/) (2025)
- [An efficient scheme based on graph centrality to select nodes for training for effective learning](https://mlsanity.com/paper/2104.14123/) (2021)
- [Identifying Influential Nodes in Two-mode Data Networks using Formal Concept Analysis](https://mlsanity.com/paper/2109.03372/) (2021)
- [Identifying critical nodes in complex networks by graph representation learning](https://mlsanity.com/paper/2201.07988/) (2022)
- [Natural Language Processing for Drug Discovery Knowledge Graphs: promises and pitfalls](https://mlsanity.com/paper/2310.15572/) (2023)
- [Network World Models as Environments for Algorithm Design on Complex Systems](https://mlsanity.com/paper/2610.01048/) (2026)

## Abstract (arXiv)

Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-r ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and k-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) model, VoteRank outperforms the traditional benchmark methods on both spreading speed and final affected scale. What's more, VoteRank is also superior to other group-spreader identifying methods on computational time.