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A Survey on Location-Driven Influence Maximization (2204.08005v2)

Published 17 Apr 2022 in cs.SI and cs.GT

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.

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Authors (8)
  1. Taotao Cai (8 papers)
  2. Quan Z. Sheng (91 papers)
  3. Xiangyu Song (13 papers)
  4. Jian Yang (505 papers)
  5. Shuang Wang (159 papers)
  6. Wei Emma Zhang (46 papers)
  7. Jia Wu (93 papers)
  8. Philip S. Yu (592 papers)
Citations (5)

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