Time-aware reciprocity prediction in trust network

Study of reciprocity helps to find influential factors for users building relationships, which greatly facilitates the social behavior understanding in trust networks. In the previous literature, the dynamics of both network structure and user generated content are rarely considered. Our investigation of the available timing information from a real-world network demonstrates that time delay has significant impact on reciprocity formation. In particular, we find structural factors possess greater effect on short-term reciprocity while factors based on user generated content become more important for long-term reciprocity. Based on the empirical analysis, we redefine the reciprocity prediction problem as a learning task specific to each pair of users with different reciprocal delays. Evaluations show that our time-aware framework eventually outperforms the conventional classifiers that ignore the temporal information. Meanwhile, we tackle the problem of concept drift through fitting the evolving trend of features for Naive Bayes and performing periodic retraining for Logistic Regression classifiers, respectively.