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Article

Significant Geo-Social Group Discovery over Location-Based Social Network †

by 1,* and 2
1
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
2
Faculty of Arts, Design and Architecture, School of Built Environment, The University of New South Wales, Sydney, NSW 2052, Australia
*
Author to whom correspondence should be addressed.
This manuscript is extension version of the conference paper: Li, Wei and Sisi Zlatanova. Effective Geo-Social Group Detection in Location-Based Social Networks. 2019 IEEE International Symposium on Multimedia (ISM). IEEE, 9–11 December 2019, San Diego, CA, USA.
Academic Editor: Giorgio Terracina
Sensors 2021, 21(13), 4551; https://doi.org/10.3390/s21134551
Received: 4 June 2021 / Revised: 18 June 2021 / Accepted: 25 June 2021 / Published: 2 July 2021
(This article belongs to the Section Sensor Networks)
Geo-social community detection over location-based social networks combining both location and social factors to generate useful computational results has attracted increasing interest from both industrial and academic communities. In this paper, we formulate a novel community model, termed geo-social group (GSG), to enforce both spatial and social factors to generate significant computational patterns and to investigate the problem of community detection over location-based social networks. Specifically, GSG detection aims to extract all group-venue clusters, where users are similar to each other in the same group and they are located in a minimum covering circle (MCC) for which the radius is no greater than a distance threshold γ. Then, we present a GSGD algorithm following a three-step paradigm to enumerate all qualified GSGs in a large network. We propose effective optimization techniques to efficiently enumerate all communities in a network. Furthermore, we extend a significant GSG detection problem to top-k geo-social group (TkGSG) mining. Rather than extracting all qualified GSGs in a network, TkGSG aims to return k feasibility groups to guarantee the diversity. We prove the hardness of computing the TkGSGs. Nevertheless, we propose the effective greedy approach with a guaranteed approximation ratio of 11/e. Extensive empirical studies on real and synthetic networks show the superiority of our algorithm when compared with existing methods and demonstrate the effectiveness of our new community model and the efficiency of our optimization techniques. View Full-Text
Keywords: geo-spatial analysis; spatial information; location-based service (LBS); location-based social network (LBSN); community detection geo-spatial analysis; spatial information; location-based service (LBS); location-based social network (LBSN); community detection
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MDPI and ACS Style

Li, W.; Zlatanova, S. Significant Geo-Social Group Discovery over Location-Based Social Network. Sensors 2021, 21, 4551. https://doi.org/10.3390/s21134551

AMA Style

Li W, Zlatanova S. Significant Geo-Social Group Discovery over Location-Based Social Network. Sensors. 2021; 21(13):4551. https://doi.org/10.3390/s21134551

Chicago/Turabian Style

Li, Wei, and Sisi Zlatanova. 2021. "Significant Geo-Social Group Discovery over Location-Based Social Network" Sensors 21, no. 13: 4551. https://doi.org/10.3390/s21134551

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