Inferring Directions of Undirected Social Ties

Abstract

The directionality is a significant but inherent property of social ties, though usually ignored in undirected social networks due to its invisibility. However, we believe most social ties are natively directed, and the perception of directionality can improve our understanding about the network structures and further benefit other tasks upon social networks. In this study, we address the latent tie direction inference problem in undirected social networks. We engage in the investigation of directionality on real-world large-scale directed social networks and summarize our findings using four patterns. Upon that we propose a family of ReDirect approaches, including ReDirect-N, ReDirect-T and ReDirect-One, to inferring the hidden directions of undirected social ties based on the network topology only. ReDirect can incorporate with other predictive tasks, and introduce supervision to improve performance. We also present a simple but effective strategy to construct self-labeled data. Experimental results show that even without external information, our approach can recover the directions of networks effectively. Moreover, we find the ReDirect approaches can benefit the predictive tasks remarkably in an experimental study on link prediction. The ReDirect family can be a beneficial general data preprocess tool for various network analysis tasks by uncovering the hidden directions.