Efficient Similarity-Aware Influence Maximization in Geo-Social Network
| Autor: | Kai Zheng, Xiaofang Zhou, Xuanhao Chen, Rui Sun, Guanfeng Liu, Yan Zhao |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Online and offline
Similarity-aware Computer science media_common.quotation_subject 02 engineering and technology Machine learning computer.software_genre Mathematical model Promotion (rank) 020204 information systems Similarity (psychology) 0202 electrical engineering electronic engineering information engineering Social media Greedy algorithm Probability Q measurement media_common Consumption (economics) Measurement Social network business.industry Social networking (online) Sun Geo-social networks Maximization Influence maximization Computer Science Applications Computational Theory and Mathematics Artificial intelligence business computer Upper bound Information Systems |
| Zdroj: | Chen, X, Zhao, Y, Liu, G, Sun, R, Zhou, X & Zheng, K 2020, ' Efficient Similarity-aware Influence Maximization in Geo-social Network ', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 10, pp. 4767-4780 . https://doi.org/10.1109/TKDE.2020.3045783 |
| ISSN: | 2326-3865 1041-4347 |
| DOI: | 10.1109/tkde.2020.3045783 |
| Popis: | With the explosion of GPS-enabled smartphones and social media platforms, geo-social networks are increasing as tools for businesses to promote their products or services. Influence maximization, which aims to maximize the expected spread of influence in the networks, has drawn increasing attention. However, most recent work tries to study influence maximization by only considering geographic distance, while ignoring the influence of users' spatio-temporal behavior on information propagation or location promotion, which can often lead to poor results. To relieve this problem, we propose a Similarity-aware Influence Maximization (SIM) model to efficiently maximize the influence spread by taking the effect of users' spatio-temporal behavior into account, which is more reasonable to describe the real information propagation. We first calculate the similarity between users according to their historical check-ins, and then we propose a Propagation to Consumption (PTC) model to capture both online and offline behaviors of users. Finally, we propose two greedy algorithms to efficiently maximize the influence spread. The extensive experiments over real datasets demonstrate the efficiency and effectiveness of the proposed algorithms. With the explosion of GPS-enabled smartphones and social media platforms, geo-social networks are increasing as tools for businesses to promote their products or services. Influence maximization, which aims to maximize the expected spread of influence in the networks, has drawn increasing attention. However, most recent work tries to study influence maximization by only considering geographic distance, while ignoring the influence of users' spatio-temporal behavior on information propagation or location promotion, which can often lead to poor results. To relieve this problem, we propose a Similarity-aware Influence Maximization (SIM) model to efficiently maximize the influence spread by taking the effect of users' spatio-temporal behavior into account, which is more reasonable to describe the real information propagation. We first calculate the similarity between users according to their historical check-ins, and then we propose a Propagation to Consumption (PTC) model to capture both online and offline behaviors of users. Finally, we propose two greedy algorithms to efficiently maximize the influence spread. The extensive experiments over real datasets demonstrate the efficiency and effectiveness of the proposed algorithms. |
| Databáze: | OpenAIRE |
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