A0981
Title: Inferring diffusion structures of heterogeneous network cascade
Authors: Yubai Yuan - Penn State University (United States) [presenting]
Abstract: Network cascade refers to diffusion processes in which outcome changes within part of an interconnected population trigger a sequence of changes across the entire network. These cascades are governed by underlying diffusion networks, which are often latent. Inferring such networks is critical for understanding cascade pathways, uncovering Granger causality of interaction mechanisms among individuals, and enabling tasks such as forecasting or maximizing information propagation. A novel double mixture directed graph model is proposed for inferring multi-layer diffusion networks from cascade data. The proposed model represents cascade pathways as a mixture of diffusion networks across different layers, effectively capturing the strong heterogeneity present in real-world cascades. Additionally, the model imposes layer-specific structural constraints, enabling diffusion networks at different layers to capture complementary cascading patterns at the population level. A key advantage of the model is its convex formulation, which allows establishing both statistical and computational guarantees for the resulting diffusion network estimates. Extensive simulation studies are conducted to demonstrate the model's performance in recovering diverse diffusion structures.
