Kernel-Based Structural-Temporal Cascade Learning for Popularity Prediction

Abstract

One of the main objectives of information cascade popularity prediction is to forecast the future size of a cascade given the observed propagation information. It is an enabling step for many practical applications (e.g., advertisement, academic writing, etc.). Recent advances in neural networks have spurred a few deep learning-based cascade models, which preserve the structural features of information cascades with node embedding and graph neural networks. However, efforts in cascade graph learning as well as its internal temporal dependency, existing methods mainly focus on node-level similarity learning, ignoring the structural equivalence among different sub-graphs that are more informative for information diffusion prediction. Towards this, we present a kernel-based structural-temporal cascade learning model, called CasKernel, to explicitly estimate and encode the structural similarity of cascades with the graph kernels. Moreover, we employ a non sequential process to address the temporal dependency, which can be used to facilitate information popularity prediction. Experiments conducted on both tweets propagation network and academic citation network demonstrate the effectiveness of our method.