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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
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
Bagci H, Karagoz P (2016) Context-aware location recommendation by using a random walk-based approach. Knowl Inf Syst 47(2):241–260
Bao J, Zheng Y, Wilkie D, Mokbel MF (2015) Recommendations in location-based social networks: a survey. GeoInformatica 19(3):525–565
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
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
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
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
Wu Z, Zou M (2014) An incremental community detection method for social tagging systems using locality-sensitive hashing. Neural Netw 58:14–28
Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Tech (TIST) 6(3):29
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).
Conflict of interest
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.
This article does not contain any studies with human participants or animals performed by any of the authors.
Communicated by J. Park.
Rights and permissions
About this article
Cite this article
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). https://doi.org/10.1007/s00500-017-2627-2
- Location-focused online communities
- M-triadic concepts
- Time series triadic concepts
- Social evolution