  Understanding information cascades in networks is a fundamental issue in
numerous applications. Current researches often sample cascade information into
several independent paths or subgraphs to learn a simple cascade
representation. However, these approaches fail to exploit the hierarchical
semantic associations between different modalities, limiting their predictive
performance. In this work, we propose a novel Hierarchical Information
Enhancement Network (HIENet) for cascade prediction. Our approach integrates
fundamental cascade sequence, user social graphs, and sub-cascade graph into a
unified framework. Specifically, HIENet utilizes DeepWalk to sample cascades
information into a series of sequences. It then gathers path information
between users to extract the social relationships of propagators. Additionally,
we employ a time-stamped graph convolutional network to aggregate sub-cascade
graph information effectively. Ultimately, we introduce a Multi-modal Cascade
Transformer to powerfully fuse these clues, providing a comprehensive
understanding of cascading process. Extensive experiments have demonstrated the
effectiveness of the proposed method.
