Inferring Diffusion Structures of Heterogeneous Network Cascade

TL;DR

This paper introduces a convex double mixture directed graph model to infer multi-layer diffusion networks from cascade data, capturing heterogeneity and layered structures in real-world networks, with proven guarantees and practical applications.

Contribution

It presents a novel convex model for inferring multi-layer diffusion networks that accounts for heterogeneity and layered patterns in cascade data.

Findings

Effective recovery of diverse diffusion structures in simulations

Application to social science cascades reveals underlying diffusion networks

Model provides statistical and computational guarantees

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. In this project, we propose a novel double mixture directed graph model 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 our model is its convex formulation, which allows us to establish both statistical and computational guarantees for the resulting diffusion network estimates. We conduct extensive simulation studies to demonstrate the model's performance in recovering diverse diffusion structures. Finally, we apply the proposed method to analyze cascades of research topics in the social sciences across U.S. universities, revealing the underlying diffusion networks of research topic propagation among institutions.