Abstract
A series of recent studies formulated the diffusion prediction problem as a sequence prediction
task and proposed several sequential models based
on recurrent neural networks. However, nonsequential properties exist in real diffusion cascades, which do not strictly follow the sequential
assumptions of previous work. In this paper, we
propose a hierarchical diffusion attention network
(HiDAN), which adopts a non-sequential framework and two-level attention mechanisms, for diffusion prediction. At the user level, a dependency
attention mechanism is proposed to dynamically
capture historical user-to-user dependencies and
extract the dependency-aware user information. At
the cascade (i.e., sequence) level, a time-aware in-
fluence attention is designed to infer possible future
user’s dependencies on historical users by considering both inherent user importance and time decay
effects. Significantly higher effectiveness and ef-
ficiency of HiDAN over state-of-the-art sequential
models are demonstrated when evaluated on three
real diffusion datasets. The further case studies illustrate that HiDAN can accurately capture diffusion dependencies