  Recently, information cascade prediction has attracted increasing interest
from researchers, but it is far from being well solved partly due to the three
defects of the existing works. First, the existing works often assume an
underlying information diffusion model, which is impractical in real world due
to the complexity of information diffusion. Second, the existing works often
ignore the prediction of the infection order, which also plays an important
role in social network analysis. At last, the existing works often depend on
the requirement of underlying diffusion networks which are likely unobservable
in practice. In this paper, we aim at the prediction of both node infection and
infection order without requirement of the knowledge about the underlying
diffusion mechanism and the diffusion network, where the challenges are
two-fold. The first is what cascading characteristics of nodes should be
captured and how to capture them, and the second is that how to model the
non-linear features of nodes in information cascades. To address these
challenges, we propose a novel model called Deep Collaborative Embedding (DCE)
for information cascade prediction, which can capture not only the node
structural property but also two kinds of node cascading characteristics. We
propose an auto-encoder based collaborative embedding framework to learn the
node embeddings with cascade collaboration and node collaboration, in which way
the non-linearity of information cascades can be effectively captured. The
results of extensive experiments conducted on real-world datasets verify the
effectiveness of our approach.
