🤖 AI Summary
Traditional influence maximization (IM) overlooks geographic constraints, limiting its applicability in spatially embedded social networks. This survey systematically reviews location-aware IM research over the past decade, unifying advancements along three dimensions: application motivations, diffusion modeling, and algorithmic acceleration—thereby charting the evolution of spatiotemporal joint optimization. We propose a novel paradigm integrating geographic-spatial features into IM, innovatively unifying location-aware propagation models (e.g., GeoIC, LIC), spatial indexing techniques (e.g., R-tree, GeoHash), and hybrid acceleration strategies combining heuristics with learning-based methods. The work establishes a structured knowledge framework, categorizing seven representative location-enhanced application scenarios and four core algorithmic architectures. Our synthesis provides both a theoretical benchmark and a reusable practical guide for spatiotemporal influence analysis in real-world social networks.
📝 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.
