• DocumentCode
    1823095
  • Title

    Link prediction in human mobility networks

  • Author

    Yang Yang ; Chawla, Nitesh V. ; Basu, Prithwish ; Prabhala, Bhaskar ; La Porta, Tom

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Notre Dame, Notre Dame, IN, USA
  • fYear
    2013
  • fDate
    25-28 Aug. 2013
  • Firstpage
    380
  • Lastpage
    387
  • Abstract
    The understanding of how humans move is a long-standing challenge in the natural science. An important question is, to what degree is human behavior predictable? The ability to foresee the mobility of humans is crucial from predicting the spread of human to urban planning. Previous research has focused on predicting individual mobility behavior, such as the next location prediction problem. In this paper we study the human mobility behaviors from the perspective of network science. In the human mobility network, there will be a link between two humans if they are physically proximal to each other. We perform both microscopic and macroscopic explorations on the human mobility patterns. From the microscopic perspective, our objective is to answer whether two humans will be in proximity of each other or not. While from the macroscopic perspective, we are interested in whether we can infer the future topology of the human mobility network. In this paper we explore both problems by using link prediction technology, our methodology is demonstrated to have a greater degree of precision in predicting future mobility topology.
  • Keywords
    social networking (online); future mobility topology; human mobility networks; human mobility patterns; link prediction technology; urban planning; Conferences; Facebook; Network topology; Protocols; Time series analysis; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
  • Conference_Location
    Niagara Falls, ON
  • Type

    conf

  • Filename
    6785734