Joint Learning of Embedding-Based Parent Components and Information Diffusion for Social Networks

Abstract

Diffusion on social networks refers to the phenomena that opinions are spread via connected nodes. Given a set of observed cascades, the underlying diffusion process can be inferred for social network analysis. Earlier studies for modeling the diffusion process often assume that the activation of a node depends independently on the activations of its neighbors (or called parent nodes). Nevertheless, the activation of a node also depends on the connectivity of its neighbors. For instance, the opinions from the neighbors of the same closely connected social group are often similar, and thus those neighbors exhibit similar influence. Some recent studies incorporate the structural dependency of neighbors as connected components, which allow more accurate diffusion models to be inferred. However, the effectiveness of such component-based models often depends on how the components are identified. Existing methods are not designed to directly preserve the local connectivity of neighbors. In this paper, we propose to incorporate network embedding to enhance the performance of component-based diffusion models in social networks. In particular, we embed nodes in a social network in a latent vector space with local connectivity of the nodes preserved. Parent component identification then becomes a clustering task in the embedding space. A united probabilistic framework is proposed so that the parent components and the component-based diffusion models can be inferred simultaneously using a two-level EM algorithm based on observed information cascades. For performance evaluation, we apply the proposed model to both synthetic and real world data sets with promising results obtained. The empirical results also show how the use of the embedding-based framework can enhance both the component identification and the diffusion model.