Efficient Sample Location Selection for Query Zone in Geo-Social Networks

Kejian Tang,Hui Zhu,Xiaoyu Zhu,Ming Zhong,Tao Zhan,Jianxin Li,Shaohui Zhan,Tieyun Qian,Qian Zeng,Yuanyuan Zhu,Rosa M Benito

doi:10.1155/2021/2581288

Abstract

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.

Highlights

We develop two sample location selection approaches denoted by Facility Location Based Sampling (FLS) [4] and Conditional Facility Location Based Sampling (CFLS), respectively
Sample location selection is crucial for the distance-aware influence maximization (DAIM) problem in geo-social networks. e previous works mainly select sample locations by simple methods such as random sampling or equal cell sampling, which can achieve a good online seeding performance within a moderate precomputation overhead
We propose the conception of query zone and reasonably formulate a novel problem of sample location selection for a given query zone, and we devise two methods to select sample locations, denoted by Facility Location Based Sampling (FLS) and Conditional Facility Location Based Sampling (CFLS), respectively

Summary

Introduction

E widely used geo-position-enabled devices (mobile phone, tablets, laptops, etc.) and services (geolocation, geocoding, geotagging, etc.) allow social networks to connect users with local places and events that match their interests. There are currently a lot of popular geo-social network applications like Yelp, Gowalla, Facebook Places, and Foursquare. Due to the obvious implication, many researches turn to focus on taking location information into account in the influence maximization problem of geo-social networks. Different from the traditional influence maximization, a typical scenario of influence maximization in geo-social networks is to promote a specific location like a newly opened restaurant or an upcoming sale activity, which is called the query location. The users near the query location are more valuable to be influenced, because they are more likely to visit the location

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Methods

Conclusion