Information Diffusion Prediction via Exploiting Cascade Relationship Diversity

Xigang Sun, Jingya Zhou, Zhen Wu, Jie Wang

Abstract: Information diffusion can be regarded as the process of multi-user collaboration to deliver information. How to predict the cascade size is a fundamental task and has many applications such as rumor detection, product marketing, etc. Recent works attempt to mine temporal and structural characteristics hidden in the information cascade based on deep learning models. As we know, the complicated interactions between nodes are critical for cascade size prediction, and these interactions are usually hidden in the multiple types of cascade relationships. However, the cascade relationship diversity has not been comprehensively exploited by current studies. In this paper, we propose a novel model named CTformer, which leverages the global receptive field of Transformer to make accurate prediction. Specifically, CTformer takes advantage of both global position encoding and bias matrices to explore cascade relationship diversity. Extensive evaluation results on multiple real-world datasets show that CTformer achieves significant performance gains over the state-of-the-art methods.