Predicting the Popularity of Online Content by Modeling the Social Influence and Homophily Features - Shang, Yingdan; Zhou, Bin; Zeng, Xiang; Wang, Ye; Yu, Han; Zhang, Zhong (Frontiers in Physics, 2022)
Predicting the popularity of online contents on social network can bring considerable economic benefits to companies and marketers, it has wide application in viral marketing, content recommendation, early warning of social unrest, etc. The diffusion process of online contents are often a complex combination of both social influence and homophily, however, existing works either only consider the social influence or homophily of early infected users, and fail to model the joint effect of social influence and homophily when predicting the future popularity. In this paper, we aim to develop a framework to unify the social influence and homophily in popularity prediction. We use an unsupervised graph neural network framework to model the nondirectional social homophily and integrate attention mechanism with graph neural network framework to learn the directional and heterogenous social relationship for generating social influence representation. On the other hand, existing researches often overlook the social group characteristics of early infected users, we try to divide users into different social groups based on user interest and learn the social group representation from clusters. We integrate the social influence, homophily and social group representation of early infected users to make popularity prediction. Experiments on real datasets show that the proposed method significantly improves the prediction accuracy comparing with the latest methods, which confirms the importance of jointly model social influence and homophily and shows that social group characteristic is an important predictor in popularity prediction task.