Abstract
Information diffusion prediction is an important
task which studies how information items spread
among users. With the success of deep learning
techniques, recurrent neural networks (RNNs) have
shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction which aims at guessing the next
influenced user or macroscopic diffusion prediction which estimates the total numbers of influenced users during the diffusion process. To the
best of our knowledge, no previous works have
suggested a unified model for both microscopic
and macroscopic scales. In this paper, we propose a novel multi-scale diffusion prediction model
based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information
into the RNN-based microscopic diffusion model
by addressing the non-differentiable problem. We
also employ an effective structural context extraction strategy to utilize the underlying social graph
information. Experimental results show that our
proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic
diffusion predictions on three real-world datasets