InDNI: An Infection Time Independent Method for Diffusion Network Inference

Guoxin Chen, Yongqing Wang, Jiangli Shao, Boshen Shi, Huawei Shen, Xueqi Cheng

Abstract: Diffusion network inference aims to reveal the message propagation process among users and has attracted many research interests due to the fundamental role it plays in some real applications, such as rumor-spread forecasting and epidemic controlling. Most existing methods tackle the task with exact node infection time. However, collecting infection time information is time-consuming and labor-intensive, especially when information flows are huge and complex. To combat the problem, we propose a new diffusion network inference algorithm that only relies on infection states. The proposed method first encodes several observation states into a node infection matrix and then obtains the node embedding via the variational autoencoder (VAE). Nodes with the least Wasserstein distance of embeddings are predicted for existing propagation edges. Meanwhile, to reduce the complexity, a novel clustering-based filtering strategy is designed for selecting latent propagation edges. Extensive experiments show that the proposed model outperforms the state-of-the-art infection time independent models while demonstrating comparable performance over infection time based models.