New Insights and Methods For Predicting Face-To-Face Contacts

The prediction of new links in social networks is a challenging task. In this paper, we focus on predicting links in networks of face-to-face spatial proximity by using information from online social networks, such as co-authorship networks in DBLP, and a number of node level attributes. First, we analyze influence factors for the link prediction task. Then, we propose a novel method that combines information from different networks and node level attributes for the prediction task: We introduce an unsupervised link prediction method based on rooted random walks, and show that it outperforms state-of-the-art unsupervised link prediction methods. We present an evaluation using three real-world datasets. Furthermore, we discuss the impact of our results and of the insights we glean in the field of link prediction and human contact behavior.