# Robust Influence Maximization

arXiv: https://arxiv.org/abs/1601.06551 · HTML: https://mlsanity.com/paper/1601.06551/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2016.

## Built on by (most cited first, 5 of 5)

- [CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks](https://mlsanity.com/paper/2107.03603/) (2021)
- [Network Inference from a Mixture of Diffusion Models for Fake News Mitigation](https://mlsanity.com/paper/2008.03450/) (2020)
- [Online Learning of Independent Cascade Models with Node-level Feedback](https://mlsanity.com/paper/2109.02519/) (2021)
- [Bayesian Optimization of Functions over Node Subsets in Graphs](https://mlsanity.com/paper/2405.15119/) (2024)
- [Toward Trustworthy AI: Multi-Target Adversarial Attacks and Robust Defenses for Continuous Data Summarization](https://mlsanity.com/paper/2606.11804/) (2026)

## Abstract (arXiv)

In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem --- the task of finding k seed nodes in a social network to maximize the influence spread. We propose the problem of robust influence maximization, which maximizes the worst-case ratio between the influence spread of the chosen seed set and the optimal seed set, given the uncertainty of the parameter input. We design an algorithm that solves this problem with a solution-dependent bound. We further study uniform sampling and adaptive sampling methods to effectively reduce the uncertainty on parameters and improve the robustness of the influence maximization task. Our empirical results show that parameter uncertainty may greatly affect influence maximization performance and prior studies that learned influence probabilities could lead to poor performance in robust influence maximization due to relatively large uncertainty in parameter estimates, and information cascade based adaptive sampling method may be an effective way to improve the robustness of influence maximization.