Journal
ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE
Volume 49, Issue 2, Pages 619-636Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/23998083211016871
Keywords
Geographic context; link prediction; social networks
Funding
- Economic and Social Research Council [ES/J50001X/1]
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The study utilized Random Forest predictors to analyze future encounters among students, finding that network and social features hold the highest discriminatory power in predicting future encounters.
Space, time and the social realm are intrinsically linked. While an array of studies have tried to untangle these factors and their influence on human behaviour, hardly any have taken their effects into account at the same time. To disentangle these factors, we try to predict future encounters between students and assess how important social, spatial and temporal features are for prediction. We phrase our problem of predicting future encounters as a link-prediction problem and utilise set of Random Forest predictors for the prediction task. We use data collected by the Copenhagen network study; a study unique in scope and scale and tracks 847 students via mobile phones over the course of a whole academic year. We find that network and social features hold the highest discriminatory power for predicting future encounters.
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