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

Christoph Scholz (1) , Martin Atzmueller (1) , Gerd Stumme (1) , Alain Barrat (2, 3, 4, 5) , Ciro Cattuto (4)

1 Knowledge and Data Engineering Group
2 CPT - E5 Physique statistique et systèmes complexes
3 CPT - Centre de Physique Théorique - UMR 6207
4 ISI - Data Science Laboratory
5 CPT - Centre de Physique Théorique - UMR 7332

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.

Domaines: Web; Autre [cs.OH]

Christoph Scholz, Martin Atzmueller, Gerd Stumme, Alain Barrat, Ciro Cattuto. New Insights and Methods for Predicting Face-To-Face Contacts. 7th International Conference on Weblogs and Social Media (ICWSM-13), Jul 2013, Boston, United States. ⟨hal-00842898⟩