Multidimensional Data Analysis of Location Based Social Network

  • Authors

    • Shaik Mastan Vali
    • P. Sujatha
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.36.24534
  • Location based social networks, bright kite, Gowalla, comparison, impact of LBSN.
  • Abstract

    Long range interpersonal communication benefits gather data on clients' social contacts, make an expansive interrelated informal organization, and open to clients how they are connected to others in the system. The basic of an OSN contains of customized client profiles, which for the most part encase interests (e.g. bought in intrigue gatherings), perceiving data (e.g. name and photograph), and individual contacts (e.g. rundown of connected clients, alleged "companions"). The ability to accumulate and inspect such information conveys particular chances to perceive the central belief systems of interpersonal organizations, their creation, movement and attributes. These sorts of informal communities are classified to be specific scholarly, general and area based interpersonal organizations. In this paper, we concentrated on the area based interpersonal organizations. Here, we investigations the diverse kinds of information that utilizations in area based interpersonal organizations and furthermore examine the effect of online datasets on neighborhood based interpersonal organization.

     

     

     

  • References

    1. [1] Valverde-Rebaza JC, Roche M, Poncelet P & De Andrade Lopes A, “The role of location and social strength for friendship prediction in location-based social networksâ€, Information Processing & Management, Vol.54, No.4, (2018), pp.475-489.

      [2] Bao J, Zheng Y, Wilkie D & Mokbel MF, “A survey on recommendations in location-based social networksâ€, ACM Transaction on Intelligent Systems and Technology, (2013), pp.1-30.

      [3] Chen T, Kaafar MA & Boreli R, “The Where and When of Finding New Friends: Analysis of a Location-based Social Discovery Networkâ€, ICWSM, (2013), pp.61-70.

      [4] Yu Y & Chen X, “A survey of point-of-interest recommendation in location-based social networksâ€, Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence, Vol.130, (2015), pp.53-60.

      [5] Lian D, Zhu Y, Xie X & Chen E, “Analyzing location predictability on location-based social networksâ€, Pacific-Asia Conference on Knowledge Discovery and Data Mining, (2014), pp.102-113.

      [6] Wang H, Terrovitis M & Mamoulis N, “Location recommendation in location-based social networks using user check-in dataâ€, 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, (2013), pp.374-383.

      [7] Doan TN, Chua FCT & Lim EP, “On neighborhood effects in location-based social networksâ€, IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Vol.1, (2015), pp.477-484.

      [8] Alrayes F & Abdelmoty A, “Privacy concerns in location-based social networksâ€, (2014), pp.1-10.

      [9] Wang G, Schoenebeck SY, Zheng H & Zhao BY, “Will Check-in for Badge; Understanding Bias and Misbehavior on Location-Based Social Networks. In ICWSM, (2016), pp.417-426.

      [10] Persia F & D'Auria D, “A Survey of Online Social Networks: Challenges and Opportunitiesâ€, IEEE International Conference on Information Reuse and Integration (IRI), (2017), pp.614-620.

      [11] Yousukkee S, “Survey of analysis of user behavior in online social networkâ€, IEEE International Conference on Management and Innovation Technology (MITicon), (2016), pp.MIT-128.

      [12] Zhang S, Zheng X & Hu C, “A survey of semantic similarity and its application to social network analysisâ€, IEEE International Conference on Big Data (Big Data), (2015), pp.2362-2367.

      [13] Kumar H, Jain S & Srivastava R, “Risk analysis of online social networksâ€, IEEE International Conference on Computing, Communication and Automation (ICCCA), (2016), pp.846-851.

      [14] Li N & Chen G, “Analysis of a location-based social networkâ€, IEEE International Conference on Computational Science and Engineering, Vol.4, (2009), pp.263-270.

      [15] Chang J & Sun E, “Location 3: How users share and respond to location-based data on social networking sitesâ€, Fifth International AAAI Conference on Weblogs and Social Media, (2011), pp.74-80.

      [16] Cho E, Myers SA & Leskovec J, “Friendship and mobility: user movement in location-based social networksâ€, 17th ACM SIGKDD international conference on Knowledge discovery and data mining, (2011), pp.1082-1090.

      [17] Cranshaw J, Toch E, Hong J, Kittur A & Sadeh N, “Bridging the gap between physical location and online social networksâ€, Proceedings of the 12th ACM international conference on Ubiquitous computing, (2010) pp.119-128.

      [18] Toch E, Cranshaw J, Drielsma PH, Tsai JY, Kelley PG, Springfield J, Cranor L, Hong J & Sadeh N, “Empirical models of privacy in location sharingâ€, 12th ACM international conference on Ubiquitous computing, (2010), pp.129-138.

  • Downloads

  • How to Cite

    Mastan Vali, S., & Sujatha, P. (2018). Multidimensional Data Analysis of Location Based Social Network. International Journal of Engineering & Technology, 7(4.36), 797-801. https://doi.org/10.14419/ijet.v7i4.36.24534

    Received date: 2018-12-21

    Accepted date: 2018-12-21

    Published date: 2018-12-09