# Influence maximization by rumor spreading on correlated networks through community identification

arXiv: https://arxiv.org/abs/1705.00630 · HTML: https://mlsanity.com/paper/1705.00630/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2017.

## Built on by (most cited first, 3 of 3)

- [A modified gravity model based on network efficiency for vital nodes identification in complex networks](https://mlsanity.com/paper/2111.01526/) (2021)
- [Evaluating importance of nodes in complex networks with local volume information dimension](https://mlsanity.com/paper/2111.13585/) (2021)
- [Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach](https://mlsanity.com/paper/2504.11245/) (2025)

## Abstract (arXiv)

The identification of the minimal set of nodes that maximizes the propagation of information is one of the most relevant problems in network science. In this paper, we introduce a new method to find the set of initial spreaders to maximize the information propagation in complex networks. We evaluate this method in assortative networks and verify that degree-degree correlation plays a fundamental role in the spreading dynamics. Simulation results show that our algorithm is statistically similar, regarding the average size of outbreaks, to the greedy approach in real-world networks. However, our method is much less time consuming than the greedy algorithm.