Combine the Growth of Cascades and Impact of Users for Diffusion Prediction

Information diffusion and diffusion prediction have attracted a great deal of research attention over the past decades. Existing approaches usually make predictions based on the order of the activated users, while recently, some studies have taken the social network into consideration and begun to analyze the influence of neighbors via some graph neural networks. However, they ignore the fact that the interests of users and their neighbors may dynamically change along with the growth of the cascade, and thus fail to model the potential impact of activated users. To address the above shortcomings, we proposed in this paper a deep learning model that combines the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b>ode of cascades <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b>rowth and potential <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b>mpact of users (MGI). It leverages GCNs to represent users from the social network to model their static features. Besides, we designed an attention mechanism on the cascade sequence to compute features of activated users, and added the popularity variable to model features of users in cascades. Finally, we combined the growth of cascades and impact of users in our model for diffusion prediction. We conducted extensive experiments on several real-world datasets, and the experimental results demonstrate that our model significantly outperforms the state-of-the-art methods in diffusion prediction.