  One important task in the study of information cascade is to predict the
future recipients of a message given its past spreading trajectory. While the
network structure serves as the backbone of the spreading, an accurate
prediction can hardly be made without the knowledge of the dynamics on the
network. The temporal information in the spreading sequence captures many
hidden features, but predictions based on sequence alone have their
limitations. Recent efforts start to explore the possibility of combining both
the network structure and the temporal feature. Here, we propose a new
end-to-end prediction method CasSeqGCN in which the structure and temporal
feature are simultaneously taken into account. A cascade is divided into
multiple snapshots which record the network topology and the state of nodes.
The graph convolutional network (GCN) is used to learn the representation of a
snapshot. A novel aggregation method based on dynamic routing is proposed to
aggregate node representation and the long short-term memory (LSTM) model is
used to extract temporal information. CasSeqGCN predicts the future cascade
size more accurately compared with other state-of-art baseline methods. The
ablation study demonstrates that the improvement mainly comes from the design
of the input and the GCN layer. We explicitly design an experiment to show the
quality of the cascade representation learned by our approach is better than
other methods. Our work proposes a new approach to combine the structural and
temporal features, which not only gives a useful baseline model for future
studies of cascade prediction, but also brings new insights on a wide
collection of problems related with dynamics on and of the network.
