Information diffusion prediction based on cascade sequences and social topology

Abstract

People have been increasingly sharing information via social media platforms such as Twitter and Facebook in recent years, making understanding and predicting the spread of information a hot research topic. Accurate prediction of cascades within social platforms can effectively track the information dissemination process and prevent the spread of harmful information. Previous work either did not fully exploit the potential social graph structure or chose to predict communication sequences as a diffusion graph, resulting in inconsistency with the actual diffusion situation. We propose a method that is structurally simple and effective, requiring only sequential sequences of users' spread information and social relationships among users. Our method is based on LSTM (Long Short-Term Memory) for temporal feature extraction of cascade sequences and combined with GCN (Graph Convolutional Networks) for extracting features containing node topology in social graphs for microscopic prediction of information diffusion cascades. Comparative experiments on two real social platform datasets demonstrate that our method has a better prediction effect.