Cascade2vec: Learning Dynamic Cascade Representation by Recurrent Graph Neural Networks

Abstract

An information dissemination network (i.e., a cascade) with a dynamic graph structure is formed when a novel idea or message spreads from person to person. Predicting the growth of cascades is one of the fundamental problems in social network analysis. Existing deep learning models for cascade prediction are primarily based on recurrent neural networks and representation on random walks or propagation paths. However, these models are not sufficient for learning the deep spatial and temporal features of an entire cascade. Therefore, a new model, called Cascade2vec, is proposed to learn the dynamic graph representation of cascades based on graph recurrent neural networks. To learn more effective graph-level representation of cascades, the current graph neural networks are improved by designing a graph residual block, which shares attention weights between nodes, and by transforming features through perception layers. Furthermore, the proposed graph neural network is integrated into a recurrent neural network to learn the temporal features between graphs. With this method, both the spatial and temporal characteristics of cascades are learned in Cascade2vec. The experimental results show that our method significantly reduces the mean squared logarithmic error and median squared logarithmic error by 16.1% and 12%, respectively, in the cascade prediction at one hour in the Microblog network dataset compared with strong baselines.