# A Survey on Location-Driven Influence Maximization

arXiv: https://arxiv.org/abs/2204.08005 · HTML: https://mlsanity.com/paper/2204.08005/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2022.

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

- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [Online Influence Maximization: Concept and Algorithm](https://mlsanity.com/paper/2312.00099/) (2023)
- [Towards Regret Free Slot Allocation in Billboard Advertisement](https://mlsanity.com/paper/2401.16464/) (2024)

## Abstract (arXiv)

Influence Maximization (IM), which aims to select a set of users from a social network to maximize the expected number of influenced users, is an evergreen hot research topic. Its research outcomes significantly impact real-world applications such as business marketing. The booming location-based network platforms of the last decade appeal to the researchers embedding the location information into traditional IM research. In this survey, we provide a comprehensive review of the existing location-driven IM studies from the perspective of the following key aspects: (1) a review of the application scenarios of these works, (2) the diffusion models to evaluate the influence propagation, and (3) a comprehensive study of the approaches to deal with the location-driven IM problems together with a particular focus on the accelerating techniques. In the end, we draw prospects into the research directions in future IM research.