# Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach

arXiv: https://arxiv.org/abs/1705.10442 · HTML: https://mlsanity.com/paper/1705.10442/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2017.

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

- [Finding Influencers in Complex Networks: An Effective Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2309.07153/) (2023)

## Abstract (arXiv)

Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hop-based algorithms that can easily scale to millions of nodes and billions of edges. Unlike previous heuristics, our proposed hop-based approaches can provide certain theoretical guarantees. Experimental evaluations with real OSN datasets demonstrate the efficiency and effectiveness of our algorithms.