Information Cascades Prediction With Graph Attention

Abstract

The cascades prediction aims to predict the possible information diffusion path in the future based on cascades of the social network. Recently, the existing researches based on deep learning have achieved remarkable results, which indicates the great potential to support cascade prediction task. However, most prior arts only considered either cascade features or user relationship network to predict cascade, which leads to the performance limitation because of the lack of unified modeling for the potential relationship between them. To that end, in this paper, we propose a recurrent neural network model with graph attention mechanism, which constructs a seq2seq framework to learn the spatial-temporal cascade features. Specifically, for user spatial feature, we learn potential relationship among users based on social network through graph attention network. Then, for temporal feature, a recurrent neural network is built to learn their structural context in several different time intervals based on timestamp with a time-decay attention. Finally, we predict the next user with the latest cascade representation which obtained by above method. Experimental results on two real-world datasets show that our model achieves better performance than the baselines on the both evaluation metrics of HITS and mean average precision.