A Combined Deep-Learning and Transfer-Learning Approach for Supporting Social Influence Prediction

Abstract Social influence is a phenomenon describing the spread of opinions across the population. Nowadays, social influence analysis (SIA) has a great impact. For example, viral marketing and online content recommendation are applications of SIA. Hand-crafted features, as well as domain expert knowledge, are usually required in convention social influence analysis, but they incur high costs and are not scalable. Deep learning based approaches overcome these issues. For instance, a recently used approach learned latent features of users to predict social influence. In this paper, a teleport probability τ from the page rank domain is integrated into the graph convolution network model for further enhance the performance of such an approach. In addition, a combined personalized propagation of neural predictions (CPPNP) algorithm leads to an impressive prediction accuracy when comparing with existing methods. Evaluation results on three well-known datasets reveal that optimizing τ enhances the performance of CPPNP. Such a combined deep-learning and transfer-learning approach well supports the social influence prediction