Graph Convolutional Network with Time-Based Mini-Batch for Information Diffusion Prediction

摘要

Information diffusion prediction is a fundamental task for understanding information spreading phenomenon. Many of the previous works use static social graph or cascade data for prediction. In contrast, a recently proposed deep leaning model DyHGCN [20] newly considers users' dynamic preference by using dynamic graphs and achieve better performance. However, training phase of DyHGCN is computationally expensive due to the multiple graph convolution computations. Faster training is also important to reflect users' dynamic preferences quickly. Therefore, we propose a novel graph convolutional network model with time-based mini-batch (GCNTM) to improve training speed while modeling users' dynamic preference. Time-based mini-batch is a novel input form to handle dynamic graphs efficiently. Using this input, we reduce the graph convolution computation only once per mini-batch. The experimental results on three real-world datasets show that our model performs comparable results against baseline models. Moreover, our model learns about 5.97 times faster than DyHGCN.