Location‐aware targeted influence maximization in social networks

Su, Sen; Li, Xiao; Cheng, Xiang; Sun, Chenna

doi:10.1002/asi.23931

Cited by 22 publications

(16 citation statements)

References 31 publications

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“…With the development of geographical location equipment, geographical factors play an increasingly important role in the analysis of social networks. The TR tree index structure [7] is designed for users with themed and geographic preferences for promotional products. Each tree node stores the user's themed and geographic preferences.…”

Section: Geo-social Networkmentioning

confidence: 99%

“…Each tree node stores the user's themed and geographic preferences. By traversing the TR tree in depth precedence, Su et al [7] can find the target user effectively. Zhong et al [8] propose an efficient location sampling method based on heuristic anchor selection and facility allocation technique.…”

Section: Geo-social Networkmentioning

confidence: 99%

“…Most of existing methods [5][6][7][8][9][10] have two following shortcomings when they are applied in maximum recommendation scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Maximum Recommendation in Geo-social Network for Business

2019

Teh. vjesn.

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Most of existing methods do not consider the maximum recommendation issue. Meanwhile, the methods also do not consider the negative influence in recommendation model. These two shortcomings limit further application of the recommendation system. In another word, the shortcomings not only decrease the recommendation effect but also increase the recommendation cost in the business. To remove the shortcomings, we propose a Maximum Recommendation scheme in Geo-social network for business (called as MRG). On the one hand, we identify k nodes with maximum recommendation according to the expected paid node number k. On the other hand, we exclude the negative node from the geo-social network. Based on the above innovation, we effectively increase the recommendation effect and decrease the company's recommendation cost. Meanwhile, MRG considers the negative influence to enhance the recommendation efficiency. Experimental results show that our scheme has better performance than most of the existing methods in the maximum recommendation field.

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Section: Geo-social Networkmentioning

confidence: 99%

Section: Geo-social Networkmentioning

confidence: 99%

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Maximum Recommendation in Geo-social Network for Business

2019

Teh. vjesn.

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“…Besides the location, the interests/topics of users are also taken into consideration in IM, and [35] proposed an algorithm that returns top-topics related to the query of a user. Su et al [36] take not only users' interests but also their preference for locations into account, to find the targeted users, and then seek seeds to maximize the influence for targeted users.…”

Section: Related Workmentioning

confidence: 99%

Targeted Influential Nodes Selection in Location‐Aware Social Networks

Yang

Jiang

2018

Complexity

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Given a target area and a location-aware social network, the location-aware influence maximization problem aims to find a set of seed users such that the information spread from these users will reach the most users within the target area. We show that the problem is NP-hard and present an approximate algorithm framework, namely, TarIM-SF, which leverages on a popular sampling method as well as spatial filtering model working on arbitrary polygons. Besides, for the large-scale network we also present a coarsening strategy to further improve the efficiency. We theoretically show that our approximate algorithm can provide a guarantee on the seed quality. Experimental study over three real-world social networks verified the seed quality of our framework, and the coarsening-based algorithm can provide superior efficiency.

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“…In order to solve this problem, some researchers have made a series of improvements to the greedy algorithm of the problem of maximizing influence [27]- [30], and some researchers have proposed heuristic algorithm [31]- [34] to solve this problem. In addition to improving the operational efficiency of the algorithm, there are many tasks to improve the practicability of the problem model, such as improving the accuracy of the impact propagation model by considering the theme perception factor [35], [36]; concretely mining the nodes with the greatest global impact into mining the node with the largest influence within a specific geographical location [37], [38]; the cost factor is introduced into the problem of maximizing influence [39], [40].…”

Section: Introductionmentioning

confidence: 99%

User Topic Preferences Based Influence Maximization in Overlapped Networks

Shi

Liu

et al. 2019

IEEE Access

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With the rapid development of online social networks, there is a trend of information interconnection among online social networks. User's social behaviors are no longer confined to a single network but interact in the form of overlapping networks. Studying the problem of maximizing the influence of overlapping networks can not only make the influence spread to the extent that a single network cannot reach, but also effectively control the cost of advertising while maintaining the scope of diffusion. However, the existing models of user identification and influence propagation in overlapping networks are still not efficient enough. For this reason, this paper studies the problem of maximizing influence in overlapping networks based on user interests. First, network coupling is carried out through the ''bridge'' role of overlapping network users; secondly, independent cascade model is used. On the basis of this, a User Interestbased Influence Propagation Model of the Overlay Network (UI-IPM) is designed. Finally, based on the UI-IPM model, a heuristic algorithm combined with the greedy algorithm is designed to maximize the impact of overlapping networks (UI-IPM) and achieve Influence Maximization of the Overlay Network (IMON) mining seed nodes. The experimental results show the effectiveness of IMON algorithm in terms of influence sphere and time efficiency, and also verify the efficiency of seed nodes mining in overlapping network environment compared with a single network environment.

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Location‐aware targeted influence maximization in social networks

Cited by 22 publications

References 31 publications

Maximum Recommendation in Geo-social Network for Business

Maximum Recommendation in Geo-social Network for Business

Targeted Influential Nodes Selection in Location‐Aware Social Networks

User Topic Preferences Based Influence Maximization in Overlapped Networks

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