  In the last decade, information diffusion (also known as information cascade)
on social networks has been massively investigated due to its application
values in many fields. In recent years, many sequential models including those
models based on recurrent neural networks have been broadly employed to predict
information cascade. However, the user dependencies in a cascade sequence
captured by sequential models are generally unidirectional and inconsistent
with diffusion trees. For example, the true trigger of a successor may be a
non-immediate predecessor rather than the immediate predecessor in the
sequence. To capture user dependencies more sufficiently which are crucial to
precise cascade modeling, we propose a non-sequential information cascade model
named as TAN-DRUD (Topology-aware Attention Networks with Dual Role User
Dependency). TAN-DRUD obtains satisfactory performance on information cascade
modeling through capturing the dual role user dependencies of information
sender and receiver, which is inspired by the classic communication theory.
Furthermore, TANDRUD incorporates social topology into two-level attention
networks for enhanced information diffusion prediction. Our extensive
experiments on three cascade datasets demonstrate that our model is not only
superior to the state-of-the-art cascade models, but also capable of exploiting
topology information and inferring diffusion trees.
