• DocumentCode
    679522
  • Title

    CSI: Charged System Influence Model for Human Behavior Prediction

  • Author

    Yuanjun Bi ; Weili Wu ; Yuqing Zhu

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Texas at Dallas, Dallas, TX, USA
  • fYear
    2013
  • fDate
    7-10 Dec. 2013
  • Firstpage
    31
  • Lastpage
    40
  • Abstract
    Social influence has been widely studied in areas of viral marketing, information diffusion and health care. Currently, most influence models only deal with a single influence without the interference of other influences. Also, the influence spreading in previous models must be triggered by individuals who have been activated by the influence. In this paper, we argue that it is the attraction from a specific influence makes an individual choose to spread it among multiple influences. Inspired by charged system theory in physics, a new influence model is proposed, considering individual features and social structure features. It also gives a natural description about how individuals make decisions among multiple influences. Then a novel algorithm based on this model is provided to predict human behavior. Extensive experiments on three real-world datasets demonstrate that our model and algorithm statistically outperform the state-of-the-art methods in terms of prediction accuracy.
  • Keywords
    behavioural sciences computing; social networking (online); CSI; charged system influence model; human behavior prediction; prediction accuracy; social structure features; Correlation; Force; Gaussian distribution; Integrated circuit modeling; Prediction algorithms; Predictive models; Social network services;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining (ICDM), 2013 IEEE 13th International Conference on
  • Conference_Location
    Dallas, TX
  • ISSN
    1550-4786
  • Type

    conf

  • DOI
    10.1109/ICDM.2013.136
  • Filename
    6729487