Who proposed the relationship?: recovering the hidden directions of undirected social networks

Together with the sign (positive or negative) and strength (strong or weak), the directionality is also an important property of social ties, though usually ignored in undirected social networks for its invisibility. However, we believe most social ties are natively directed, and the awareness of directionality can improve our understanding about the network structures and further benefit social network analysis and mining tasks. Thus it's appealing to study whether there exist interesting patterns about directionality in social networks and whether we can learn the directions for undirected networks based on these patterns. In this study, we engage in the investigation of directionality patterns on real-world directed social networks and summarize our findings using four consistency hypotheses. Based on these hypotheses, we propose ReDirect, an optimization framework which makes it possible to infer the hidden directions of undirected social ties based on the network topology only. This general framework can incorporate various predictive models under specific scenarios. Furthermore, we show how to improve ReDirect by introducing semi/self-supervision in the framework and how to construct the self-labeled training data using simple but effective heuristics. Experimental results show that even without external information, our approach can recover the directions of networks effectively.

Moreover, we're quite surprising to find that ReDirect can benefit predictive tasks remarkably, with a case study of link prediction. In experiments the redirected networks inferred using ReDirect are proven much more informative than original undirected ones and can improve the prediction performance significantly. It convinces us that ReDirect can be a beneficial general data preprocess tool for various network analysis and mining tasks by uncovering the hidden directions of undirected social networks.