Uncovering Multiple Diffusion Networks Using the First-Hand Sharing Pattern

Pei-Lun Liao, Chung-Kuang Chou, Ming-Syan Chen

Abstract: In our daily life, rumors are spread among people but diffusion processes and spreading paths behind rumors are usually hidden. The problem of finding this hidden process is getting more attention since after understanding the process, one can manipulate the diffusion speed of the process. In this work, we observe the pattern of information propagation that most nodes are inclined to share the first-hand information. In other words, the virality of an information piece will generally decay as it becomes rephrased or secondhand. We propose a generative model with the pattern and design the corresponding optimization method to infer both the hidden networks and transmission rates between nodes. Experimental results show that our model outperforms several state-of-the-art models on both synthetic and real datasets for network inference.