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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2204.08005 |
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| _version_ | 1866913760164184064 |
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| author | Cai, Taotao Sheng, Quan Z. Song, Xiangyu Yang, Jian Wang, Shuang Zhang, Wei Emma Wu, Jia Yu, Philip S. |
| author_facet | Cai, Taotao Sheng, Quan Z. Song, Xiangyu Yang, Jian Wang, Shuang Zhang, Wei Emma Wu, Jia Yu, Philip S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_08005 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A Survey on Location-Driven Influence Maximization Cai, Taotao Sheng, Quan Z. Song, Xiangyu Yang, Jian Wang, Shuang Zhang, Wei Emma Wu, Jia Yu, Philip S. Social and Information Networks Computer Science and Game Theory 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. |
| title | A Survey on Location-Driven Influence Maximization |
| topic | Social and Information Networks Computer Science and Game Theory |
| url | https://arxiv.org/abs/2204.08005 |