Personalized DeepInf: Enhanced Social Influence Prediction with Deep Learning and Transfer Learning

Social influence is referred to as the phenomenon that one's opinions or behaviors be affected by others. Nowadays, the potential impact of social influence analysis (SIA) is significant. For example, SIA applications can include viral marketing, online content recommendation. Convention social influence analysis uses hand-crafted features and requires domain expert knowledge. Such an approach is not scalable and introduces a high cost. To overcome these disadvantages, deep learning based approaches was introduced. One of the most recent approaches is DeepInf, which is an end-to-end framework for predicting social influence by learning user's latent features. We extended DeefInf in the current paper by integrating teleport probability from the domain of page rank into the graph convolution network (GCN) model to enhance the performance. Furthermore, we also propose an algorithm called hybrid personalized propagation of neural predictions (HPPNP), which shows an impressive performance in terms of prediction accuracy compared to existing methods. We reused the datasets from DeepInf and performed extensive experiments on Open Academic Graph, Twitter, DIGG datasets. By optimally sampling the teleport probability, the experimental results show that our model performs the best when compared with existing methods on different datasets. These results demonstrates the effectiveness of our enhanced personalized DeepInf-namely, HPPNP-in social influence prediction via both deep and transfer learning.