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.
翻译:影响最大化(IM)旨在从社会网络中挑选一组用户,以尽量增加预期受影响用户人数,这是一个常青热研究专题,其研究成果对商业营销等现实世界应用产生重大影响。过去十年中,基于地点的网络平台蓬勃发展,吸引研究人员将定位信息嵌入传统的IM研究。在这次调查中,我们从以下关键方面的角度全面审查了现有由地点驱动的IM研究:(1) 审查这些工程的应用情景,(2) 评估影响传播的传播模型,(3) 全面研究处理由地点驱动的IM问题的方法,同时特别关注加速技术。最后,我们为今后的IM研究开辟了研究方向。