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.

空间、时间和社会领域有着内在的联系。虽然一系列研究试图解开这些因素及其对人类行为的影响，但几乎没有任何研究同时考虑到它们的影响。为了解决这些因素，我们尝试预测学生之间未来的相遇，并评估社会、空间和时间特征对预测的重要性。我们将预测未来遭遇的问题描述为链接预测问题，并利用一组随机森林预测器进行预测任务。我们使用哥本哈根网络研究收集的数据；一项在范围和规模上独一无二的研究，在整个学年中通过手机跟踪了 847 名学生。我们发现网络和社交特征在预测未来遭遇方面具有最高的辨别力。