Not Enough Data?: Joint Inferring Multiple Diffusion Networks via Network Generation Priors

Abstract

Network Inference, i.e., discovering latent diffusion networks from observed cascades, has been studied extensively in recent years, leading to a series of excellent work. However, it has been observed that the accuracy of existing methods deteriorates significantly when the number of cascades are limited (compared with the large number of nodes), which is the norm in real world applications. Meanwhile, we are able to collect cascades on many different topics or over a long time period: the associated influence networks (either topic-specific or time-specific) are highly correlated while the number of cascade observations associated with each network is very limited. In this work, we propose a generative model, referred to as the MultiCascades model (MCM), to address the challenge of data scarcity by exploring the commonality between multiple related diffusion networks. MCM builds a hierarchical graphical model, where all the diffusion networks share the same network prior, e.g., the popular Stochastic Blockmodels or the latent space models. The parameters of the network priors can be effectively learned by gleaning evidence from a large number of inferred networks. In return, each individual network can be inferred more accurately thanks to the prior information. Furthermore, we develop efficient inference and learning algorithms so that MCM is scalable for practical applications. The results on both synthetic datasets and real-world datasets demonstrate that MCM infers both topic-specific and time-varying diffusion networks more accurately.