  Cascade prediction aims at modeling information diffusion in the network.
Most previous methods concentrate on mining either structural or sequential
features from the network and the propagation path. Recent efforts devoted to
combining network structure and sequence features by graph neural networks and
recurrent neural networks. Nevertheless, the limitation of spectral or spatial
methods restricts the improvement of prediction performance. Moreover,
recurrent neural networks are time-consuming and computation-expensive, which
causes the inefficiency of prediction. Here, we propose a novel method CCasGNN
considering the individual profile, structural features, and sequence
information. The method benefits from using a collaborative framework of GAT
and GCN and stacking positional encoding into the layers of graph neural
networks, which is different from all existing ones and demonstrates good
performance. The experiments conducted on two real-world datasets confirm that
our method significantly improves the prediction accuracy compared to
state-of-the-art approaches. What's more, the ablation study investigates the
contribution of each component in our method.
