Efficient Sample Location Selection for Query Zone in Geo-Social Networks
While promoting a business or activity in geo-social networks, the geographical distance between its location and users is critical. Therefore, the problem of Distance-Aware Influence Maximization (DAIM) has been investigated recently. The efficiency of DAIM heavily relies on the sample location sel...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/2581288 |
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| _version_ | 1832545942863085568 |
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| author | Kejian Tang Shaohui Zhan Tao Zhan Hui Zhu Qian Zeng Ming Zhong Xiaoyu Zhu Yuanyuan Zhu Jianxin Li Tieyun Qian |
| author_facet | Kejian Tang Shaohui Zhan Tao Zhan Hui Zhu Qian Zeng Ming Zhong Xiaoyu Zhu Yuanyuan Zhu Jianxin Li Tieyun Qian |
| author_sort | Kejian Tang |
| collection | DOAJ |
| description | While promoting a business or activity in geo-social networks, the geographical distance between its location and users is critical. Therefore, the problem of Distance-Aware Influence Maximization (DAIM) has been investigated recently. The efficiency of DAIM heavily relies on the sample location selection. Specifically, the online seeding performance is sensitive to the distance between the promoted location and its nearest sample location, and the offline precomputation performance is sensitive to the number of sample locations. However, there is no work to fully study the problem of sample location selection for DAIM in geo-social networks. To do this, we first formalize the problem under a reasonable assumption that a promoted location always adheres to the distribution of users (query zone). Then, we propose two efficient location sampling approaches based on facility location analysis, which is one of the most well-studied areas of operations research, and these two approaches are denoted by Facility Location based Sampling (FLS) and Conditional Facility Location Based Sampling (CFLS), respectively. FLS conducts one-time sample location selection, and CFLS extends the one-time sample location selection to a continuous process, so that an online advertising service can be started immediately without sampling a lot of locations. Our experimental results on two real datasets demonstrate the effectiveness and efficiency of the proposed methods. Specifically, both FLS and CFLS can achieve better performance than the existing sampling methods for the DAIM problem, and CFLS can initialize the online advertising service in a matter of seconds and achieve better objective distance than FLS after sampling a large number of sample locations. |
| format | Article |
| id | doaj-art-adba325717da4a93b9546b5f943c094b |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-adba325717da4a93b9546b5f943c094b2025-02-03T07:24:16ZengWileyComplexity1099-05262021-01-01202110.1155/2021/2581288Efficient Sample Location Selection for Query Zone in Geo-Social NetworksKejian Tang0Shaohui Zhan1Tao Zhan2Hui Zhu3Qian Zeng4Ming Zhong5Xiaoyu Zhu6Yuanyuan Zhu7Jianxin Li8Tieyun Qian9Jiangxi BranchJiangxi BranchJiangxi BranchBeijing Huitong Jincai Information and Technology Company LimitedSchool of Computer ScienceSchool of Computer ScienceSchool of Computer ScienceSchool of Computer ScienceSchool of Information TechnologySchool of Computer ScienceWhile promoting a business or activity in geo-social networks, the geographical distance between its location and users is critical. Therefore, the problem of Distance-Aware Influence Maximization (DAIM) has been investigated recently. The efficiency of DAIM heavily relies on the sample location selection. Specifically, the online seeding performance is sensitive to the distance between the promoted location and its nearest sample location, and the offline precomputation performance is sensitive to the number of sample locations. However, there is no work to fully study the problem of sample location selection for DAIM in geo-social networks. To do this, we first formalize the problem under a reasonable assumption that a promoted location always adheres to the distribution of users (query zone). Then, we propose two efficient location sampling approaches based on facility location analysis, which is one of the most well-studied areas of operations research, and these two approaches are denoted by Facility Location based Sampling (FLS) and Conditional Facility Location Based Sampling (CFLS), respectively. FLS conducts one-time sample location selection, and CFLS extends the one-time sample location selection to a continuous process, so that an online advertising service can be started immediately without sampling a lot of locations. Our experimental results on two real datasets demonstrate the effectiveness and efficiency of the proposed methods. Specifically, both FLS and CFLS can achieve better performance than the existing sampling methods for the DAIM problem, and CFLS can initialize the online advertising service in a matter of seconds and achieve better objective distance than FLS after sampling a large number of sample locations.http://dx.doi.org/10.1155/2021/2581288 |
| spellingShingle | Kejian Tang Shaohui Zhan Tao Zhan Hui Zhu Qian Zeng Ming Zhong Xiaoyu Zhu Yuanyuan Zhu Jianxin Li Tieyun Qian Efficient Sample Location Selection for Query Zone in Geo-Social Networks Complexity |
| title | Efficient Sample Location Selection for Query Zone in Geo-Social Networks |
| title_full | Efficient Sample Location Selection for Query Zone in Geo-Social Networks |
| title_fullStr | Efficient Sample Location Selection for Query Zone in Geo-Social Networks |
| title_full_unstemmed | Efficient Sample Location Selection for Query Zone in Geo-Social Networks |
| title_short | Efficient Sample Location Selection for Query Zone in Geo-Social Networks |
| title_sort | efficient sample location selection for query zone in geo social networks |
| url | http://dx.doi.org/10.1155/2021/2581288 |
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