Human mobility, social ties, and link prediction (Contributo in atti di convegno)

Type
Label
  • Human mobility, social ties, and link prediction (Contributo in atti di convegno) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/2020408.2020581 (literal)
Alternative label
  • Wang D., Pedreschi D., Song C., Giannotti F., Barabási A.-L. (2011)
    Human mobility, social ties, and link prediction
    in 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, San Diego, CA, USA, 21-24 /08 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Wang D., Pedreschi D., Song C., Giannotti F., Barabási A.-L. (literal)
Pagina inizio
  • 1100 (literal)
Pagina fine
  • 1108 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Area di valutazione 01 - Scienze matematiche e informatiche (literal)
Note
  • PuMa (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CCNR, Dept. of Physics and Computer Science, Northeastern University, Boston, USA; CNR-ISTI, Pisa, Iatly; 5Dept. of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA (literal)
Titolo
  • Human mobility, social ties, and link prediction (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-0813-7 (literal)
Abstract
  • Our understanding of how individual mobility patterns shape and impact the social network is limited, but is essential for a deeper understanding of network dynamics and evolution. This question is largely unexplored, partly due to the difficulty in obtaining large-scale society-wide data that simultaneously capture the dynamical information on individual movements and social interactions. Here we address this challenge for the first time by tracking the trajectories and communication records of 6 Million mobile phone users. We find that the similarity between two individuals' movements strongly correlates with their proximity in the social network. We further investigate how the predictive power hidden in such correlations can be exploited to address a challenging problem: which new links will develop in a social network. We show that mobility measures alone yield surprising predictive power, comparable to traditional network-based measures. Furthermore, the prediction accuracy can be signi ficantly improved by learning a supervised classifi er based on combined mobility and network measures. We believe our findings on the interplay of mobility patterns and social ties o er new perspectives on not only link prediction but also network dynamics. (literal)
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