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.

翻译：理解网络中的信息级联是众多应用中的一个基本问题。当前研究通常将级联信息采样为若干独立路径或子图，以学习简单的级联表示。然而，这些方法未能充分利用不同模态之间的分层语义关联，限制了其预测性能。本文提出了一种新颖的分层信息增强网络（HIENet）用于级联预测。该方法将基础级联序列、用户社交图以及子级联图整合到一个统一框架中。具体而言，HIENet利用DeepWalk将级联信息采样为一系列序列，随后收集用户间的路径信息以提取传播者的社交关系。此外，我们采用带时间戳的图卷积网络有效聚合子级联图信息。最终，我们引入多模态级联Transformer来强有力地融合这些线索，从而全面理解级联过程。大量实验证明了该方法的有效性。