TY - GEN
T1 - Human mobility, social ties, and link prediction
AU - Wang, Dashun
AU - Pedreschi, Dino
AU - Song, Chaoming
AU - Giannotti, Fosca
AU - Barabási, Albert László
PY - 2011/9/16
Y1 - 2011/9/16
N2 - 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 significantly improved by learning a supervised classifier based on combined mobility and network measures. We believe our findings on the interplay of mobility patterns and social ties offer new perspectives on not only link prediction but also network dynamics.
AB - 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 significantly improved by learning a supervised classifier based on combined mobility and network measures. We believe our findings on the interplay of mobility patterns and social ties offer new perspectives on not only link prediction but also network dynamics.
KW - Human mobility
KW - Link prediction
KW - Social network
UR - http://www.scopus.com/inward/record.url?scp=80052648601&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052648601&partnerID=8YFLogxK
U2 - 10.1145/2020408.2020581
DO - 10.1145/2020408.2020581
M3 - Conference contribution
AN - SCOPUS:80052648601
SN - 9781450308137
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1100
EP - 1108
BT - Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
T2 - 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Y2 - 21 August 2011 through 24 August 2011
ER -