Transfer Learning to Infer Social Ties across Heterogeneous Networks

Interpersonal ties are responsible for the structure of social networks and the transmission of information through these networks. Different types of social ties have essentially different influences on people. Awareness of the types of social ties can benefit many applications, such as recommendation and community detection. For example, our close friends tend to move in the same circles that we do, while our classmates may be distributed into different communities. Though a bulk of research has focused on inferring particular types of relationships in a specific social network, few publications systematically study the generalization of the problem of predicting social ties across multiple heterogeneous networks. In this work, we develop a framework referred to as TranFG for classifying the type of social relationships by learning across heterogeneous networks. The framework incorporates social theories into a factor graph model, which effectively improves the accuracy of predicting the types of social relationships in a target network by borrowing knowledge from a different source network. We also present several active learning strategies to further enhance the inferring performance. To scale up the model to handle really large networks, we design a distributed learning algorithm for the proposed model. We evaluate the proposed framework (TranFG) on six different networks and compare with several existing methods. TranFG clearly outperforms the existing methods on multiple metrics. For example, by leveraging information from a coauthor network with labeled advisor-advisee relationships, TranFG is able to obtain an F1-score of 90% (8%--28% improvements over alternative methods) for predicting manager-subordinate relationships in an enterprise email network. The proposed model is efficient. It takes only a few minutes to train the proposed transfer model on large networks containing tens of thousands of nodes.