Abstract: Information cascade prediction is a research hotspot in the field of social network analysis.It learns the propagation mode of information in online social media through the diffusion sequence and topology map of the information cascade.Most current models for solving this task are based on recurrent neural networks and only consider information cascading time series structure information or spatial structure information inside sequences,and cannot learn topological relationships between sequences.And the existing cascade graph structure learning methods cannot assign different weights to the neighbors of the nodes,resulting in poor association learning between the nodes.In response to the above problems,this paper proposes an information cascade sampling method based on node representation,which models the information cascade as a node representation rather than a sequence representation.This paper also proposes an information cascade prediction model based on hierarchical attention network (ICPHA),which learns the time series structure information of the node sequence through a recurrent neural network layer with self-attention mechanism,and learns the spatial structure information between node representations through a multi-head attention mechanism.By this way,ICPHA jointly models the structural information of the information cascade through a hierarchical attention network.ICPHA has achieved leading prediction results on Twitter,Memes,and Digg,and has good generalization ability.

Key words: Deep learning, Graph representation learning, Information cascade prediction, Multi-head attention mechanism, Online social media, Recurrent neural network