University of Cambridge > Talks.cam > Computer Laboratory Systems Research Group Seminar > Interdependence and Predictability of Human Mobility and Social Interactions

Interdependence and Predictability of Human Mobility and Social Interactions

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If you have a question about this talk, please contact Eiko Yoneki.

The study of the interdependence of human movement and social ties of individuals is one of the most interesting research areas in computational social science. Previous studies have shown that human movement is predictable to a certain extent at different geographic scales. One of the open problems is how to improve the prediction exploiting additional available information. In particular, one of the key questions is how to characterise and exploit the correlation between movements of friends and acquaintances to increase the accuracy of the forecasting algorithms.

In this talk I will discuss the results of our analysis of the Nokia Mobile Data Challenge dataset showing that, by means of multivariate nonlinear predictors, it is possible to exploit mobility data of friends in order to improve user movement forecasting. This can be seen as a process of discovering correlation patterns in networks of linked social and geographic data. I will also show how mutual information can be used to quantify this correlation; I will demonstrate how to use this quantity to select individuals with correlated mobility patterns in order to improve movement prediction. Finally, I will show how the exploitation of data related to friends improves dramatically the prediction with respect to the case of information of people that do not have social ties with the user.

More information about this project can be found at this URL : http://www.cs.bham.ac.uk/research/projects/nsl/mobility-prediction/

This talk is part of the Computer Laboratory Systems Research Group Seminar series.

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