Predicting information cascades holds significant practical implications, including applications in public opinion analysis, rumor control, and product recommendation. Existing approaches have generally overlooked the significance of semantic topics in information cascades or disregarded the dissemination relations. Such models are inadequate in capturing the intricate diffusion process within an information network inundated with diverse topics. To address such problems, we propose a neural-based model (named ICP-TMAN) using Topic-Aware Masked Attentive Network for Information Cascade Prediction to predict the next infected node of an information cascade. First, we encode the topical text into user representation to perceive the user-topic dependency. Next, we employ a masked attentive network to devise the diffusion context to capture the user-context dependency. Finally, we exploit a deep attention mechanism to model historical infected nodes for user embedding enhancement to capture user-history dependency. The results of extensive experiments conducted on three real-world datasets demonstrate the superiority of ICP-TMAN over existing state-of-the-art approaches.

预测信息级联具有重要的实际意义，包括在舆论分析、谣言控制和产品推荐方面的应用。现有的方法普遍忽视了信息级联中语义主题的重要性或忽视了传播关系。这些模型不足以捕捉充满各种主题的信息网络中复杂的传播过程。为了解决这些问题，我们提出了一种基于神经的模型（称为 ICP-TMAN），使用时间感知感知中号问A谨慎的氮网络为我信息C梯级磷预测信息级联的下一个受感染节点。首先，我们将主题文本编码为用户表示，以感知用户与主题的依赖关系。接下来，我们采用屏蔽注意力网络来设计扩散上下文以捕获用户上下文依赖性。最后，我们利用深度关注机制对历史受感染节点进行建模，以进行用户嵌入增强以捕获用户历史依赖性。对三个真实世界数据集进行的广泛实验的结果证明了 ICP-TMAN 相对于现有最先进方法的优越性。