Prediction of Social Influence in Social Networks

Social influence is an individual’s ability to change the thoughts or behaviors of others due to factors such as social status, social connections, and social wealth. Studying social influence, especially modeling and measuring the influence between people on large-scale social network data, is very important for product promotion, friend recommendation, expert discovery and user behavior prediction in social applications. In addition, it plays an important role in promoting the virtual business market and national public opinion monitoring. From a computational point of view, the research on social influence includes detection, modeling, measurement, and application of social influence. First, a training dataset is generated through independent cascade and linear threshold models. The influence of each node in the network is then calculated through graph convolutional network and reinforcement learning techniques. Finally, the proposed model is tested on three real-world large-scale datasets. Experimental results show that the proposed method can help predict the influence of nodes in social networks.