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.
@article{arxiv.2204.08005,
title = {A Survey on Location-Driven Influence Maximization},
author = {Taotao Cai and Quan Z. Sheng and Xiangyu Song and Jian Yang and Shuang Wang and Wei Emma Zhang and Jia Wu and Philip S. Yu},
journal= {arXiv preprint arXiv:2204.08005},
year = {2025}
}