A Survey on Location-Driven Influence Maximization (2204.08005v2)
Abstract: 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.
- Taotao Cai (8 papers)
- Quan Z. Sheng (91 papers)
- Xiangyu Song (13 papers)
- Jian Yang (505 papers)
- Shuang Wang (159 papers)
- Wei Emma Zhang (46 papers)
- Jia Wu (93 papers)
- Philip S. Yu (592 papers)