  Information propagation on social networks could be modeled as cascades, and
many efforts have been made to predict the future popularity of cascades.
However, most of the existing research treats a cascade as an individual
sequence. Actually, the cascades might be correlated with each other due to the
shared users or similar topics. Moreover, the preferences of users and
semantics of a cascade are usually continuously evolving over time. In this
paper, we propose a continuous-time graph learning method for cascade
popularity prediction, which first connects different cascades via a universal
sequence of user-cascade and user-user interactions and then chronologically
learns on the sequence by maintaining the dynamic states of users and cascades.
Specifically, for each interaction, we present an evolution learning module to
continuously update the dynamic states of the related users and cascade based
on their currently encoded messages and previous dynamic states. We also devise
a cascade representation learning component to embed the temporal information
and structural information carried by the cascade. Experiments on real-world
datasets demonstrate the superiority and rationality of our approach.
