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An efficient approach to understanding social evolution of location-focused online communities in location-based services


The booming and novel emerging promising technologies on ubiquitous computing, GPS positioning, are facilitating the development of location-based services (LBSs). Particularly, understanding the dynamic topological structures of mobile users in LBSs who visit the same physical locations has many meaningful applications including friend recommendation, location-sensitive items recommendation, and privacy management. In this paper, we proposed a novel m-triadic concept-based approach for uncovering the social evolution of location-focused online communities in LBSs. Firstly, an m-triadic concept-based location-focused online communities detection approach is presented. Further, the social evolution of the community is characterized by the time series triadic concepts in which the objectives contain the targeted users.

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  • Aissi S, Gouider MS, Sboui T et al (2015) A spatial data warehouse recommendation approach: conceptual framework and experimental evaluation[J]. Hum Centric Comput Inf Sci 5(1):1

    Article  Google Scholar 

  • Bagci H, Karagoz P (2016) Context-aware location recommendation by using a random walk-based approach. Knowl Inf Syst 47(2):241–260

    Article  Google Scholar 

  • Bao J, Zheng Y, Wilkie D, Mokbel MF (2015) Recommendations in location-based social networks: a survey. GeoInformatica 19(3):525–565

    Article  Google Scholar 

  • Bilogrevic I, Huguenin K, Mihaila S, Shokri R, Hubaux JP (2015) Predicting users’ motivations behind location check-ins and utility implications of privacy protection mechanisms. In: 22nd network and distributed system security symposium (NDSS” 15) (No. EPFL-CONF-202202)

  • Brown C, Nicosia V, Scellato S et al. (2012) The importance of being location friends: discovering location-focused online communities. In: Proceedings of the 2012 ACM workshop on online social networks, pp 31–36

  • Falkowski T (2009) Community analysis in dynamic social networks. Dissertation, University Magdeburg

  • Hao F, Min G, Chen J et al (2014a) An optimized computational model for multi-community-cloud social collaboration. IEEE Trans Serv Comput 7(3):346–358

  • Hao F, Yau SS, Min G et al (2014b) Detecting k-balanced trusted cliques in signed social networks. IEEE Internet Comput 18(2):24–31

  • Hao F, Li S, Min G, Kim HC, Yau SS, Yang LT (2015) An efficient approach to generating location-sensitive recommendations in ad-hoc social network environments. IEEE Trans Serv Comput 8(3):520–533

    Article  Google Scholar 

  • Hao F, Park DS, Min SD, Park S (2016a) Modeling a big medical data cognitive system with N-Ary formal concept analysis. In: Park J, Jin H, Jeong YS, Khan M (eds) Advanced multimedia and ubiquitous engineering. Lecture notes in electrical engineering, vol 393. Springer, Singapore

  • Hao F, Park DS, Min G, Jeong YS, Park JH (2016b) K-cliques mining in dynamic social networks based on triadic formal concept analysis.Neurocomputing 209:57–66

  • Hao F, Min G, Pei Z et al (2017) K-clique communities detection in social networks based on formal concept analysis. IEEE Syst J 11(1):250–259

  • Kang J, Yong H (2010) Mining spatio-temporal patterns in trajectory data. J Inf Process Syst 6(4):521–536

    Article  Google Scholar 

  • Palla G, Derenyi I, Farksa I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435:814–818

    Article  Google Scholar 

  • Tantipathananandh C, Berger-Wolf TY (2011) Finding communities in dynamic social networks. In: Proceedings of ICDM11, pp 1236–1241

  • Traag VA, Bruggeman J (2009) Community detection in networks with positive and negative links. Phys Rev E80(036115):1–6

    Google Scholar 

  • Wu Z, Zou M (2014) An incremental community detection method for social tagging systems using locality-sensitive hashing. Neural Netw 58:14–28

    Article  Google Scholar 

  • Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Tech (TIST) 6(3):29

    Google Scholar 

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This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2014-0-00720) supervised by the IITP (Institute for Information & communications Technology Promotion) and the National Research Foundation of Korea (No. NRF-2017R1A2B1008421) and partly supported by Fundamental Research Funds for the Central Universities, China (No. GK201703059) and Shanxi Scholarship Council of China (No. 2015-068).

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Correspondence to Doo-Soon Park.

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Fei Hao, Doo-Soon Park, Dae-Soo Sim, Min Jeong Kim, Young-Sik Jeong, Jong-Hyuk Park, and Hyung-Seok Seo have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by J. Park.

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Hao, F., Park, DS., Sim, DS. et al. An efficient approach to understanding social evolution of location-focused online communities in location-based services. Soft Comput 22, 4169–4174 (2018).

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  • LBSs
  • Location-focused online communities
  • M-triadic concepts
  • Time series triadic concepts
  • Social evolution