Towards popularity prediction of information cascades via degree distribution and deep neural networks

Abstract

Understanding paper citation dynamics and accurately predicting future citation counts of papers is of significant interest, and thus modeling citation dynamics as an information cascade has recently attracted considerable attention. Nevertheless, most of these recent deep learning-based information cascade prediction models are focused on the embedding of each individual node rather than the entire structure of the cascade graph, which limits the robustness of the model. Thus, instead of learning the representation of each node in the cascade, we propose learning the dynamic structural representation of the entire information cascade graph with the degree distribution vectors corresponding to different timestamps as the input of a sequential deep neural network, named CasDENN. Extensive experiments on datasets from academic paper citations (APS) and social media post forwards (Weibo) show a dramatic improvement over state-of-the-art baselines, where the prediction error can be reduced by approximately 8%–10% and the running time is less than 10% of the fast baseline.