  Recent years have witnessed tremendous interest in understanding and
predicting information spread on social media platforms such as Twitter,
Facebook, etc. Existing diffusion prediction methods primarily exploit the
sequential order of influenced users by projecting diffusion cascades onto
their local social neighborhoods. However, this fails to capture global social
structures that do not explicitly manifest in any of the cascades, resulting in
poor performance for inactive users with limited historical activities.
  In this paper, we present a novel variational autoencoder framework (Inf-VAE)
to jointly embed homophily and influence through proximity-preserving social
and position-encoded temporal latent variables. To model social homophily,
Inf-VAE utilizes powerful graph neural network architectures to learn social
variables that selectively exploit the social connections of users. Given a
sequence of seed user activations, Inf-VAE uses a novel expressive co-attentive
fusion network that jointly attends over their social and temporal variables to
predict the set of all influenced users. Our experimental results on multiple
real-world social network datasets, including Digg, Weibo, and Stack-Exchanges
demonstrate significant gains (22% MAP@10) for Inf-VAE over state-of-the-art
diffusion prediction models; we achieve massive gains for users with sparse
activities, and users who lack direct social neighbors in seed sets.
