# Perturb and Combine to Identify Influential Spreaders in Real-World Networks

arXiv: https://arxiv.org/abs/1807.09586 · HTML: https://mlsanity.com/paper/1807.09586/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Identifying Key Nodes for the Influence Spread using a Machine Learning Approach](https://mlsanity.com/paper/2412.01949/) (2024)

## Abstract (arXiv)

Some of the most effective influential spreader detection algorithms are unstable to small perturbations of the network structure. Inspired by bagging in Machine Learning, we propose the first Perturb and Combine (P&C) procedure for networks. It (1) creates many perturbed versions of a given graph, (2) applies a node scoring function separately to each graph, and (3) combines the results. Experiments conducted on real-world networks of various sizes with the k-core, generalized k-core, and PageRank algorithms reveal that P&C brings substantial improvements. Moreover, this performance boost can be obtained at almost no extra cost through parallelization. Finally, a bias-variance analysis suggests that P&C works mainly by reducing bias, and that therefore, it should be capable of improving the performance of all vertex scoring functions, including stable ones.