Existing approaches for information cascade prediction fall into three main categories: feature-driven methods, point process-based methods, and deep learning-based methods. Among them, deep learning-based methods, characterized by its superior learning and representation capabilities, mitigates the shortcomings inherent of the other methods. However, current deep learning methods still face several persistent challenges. In particular, accurate representation of user attributes remains problematic due to factors such as fake followers and complex network configurations. Previous algorithms that focus on the sequential order of user activations often neglect the rich insights offered by activation timing. Furthermore, these techniques often fail to holistically integrate temporal and structural aspects, thus missing the nuanced propagation trends inherent in information cascades.To address these issues, we propose the Cross-Domain Information Fusion Framework (CasCIFF), which is tailored for information cascade prediction. This framework exploits multi-hop neighborhood information to make user embeddings robust. When embedding cascades, the framework intentionally incorporates timestamps, endowing it with the ability to capture evolving patterns of information diffusion. In particular, the CasCIFF seamlessly integrates the tasks of user classification and cascade prediction into a consolidated framework, thereby allowing the extraction of common features that prove useful for all tasks, a strategy anchored in the principles of multi-task learning.

翻译：现有的信息级联预测方法主要分为三类：特征驱动方法、基于点过程的方法和基于深度学习的方法。其中，基于深度学习的方法凭借其优越的学习与表示能力，弥补了其他方法固有的不足。然而，当前的深度学习方法仍面临若干持续挑战。特别地，由于虚假关注者与复杂网络配置等因素的影响，用户属性的准确表示仍存在问题。以往侧重于用户激活顺序的算法往往忽略了激活时序所提供的丰富信息。此外，这些技术常常未能全面整合时间与结构方面的特征，从而错失了信息级联中细微的传播趋势。针对上述问题，我们提出了一种专为信息级联预测设计的跨域信息融合框架（CasCIFF）。该框架利用多跳邻域信息增强用户嵌入的鲁棒性。在进行级联嵌入时，框架有意识地融入时间戳，使其能够捕捉信息扩散的演化模式。值得注意的是，CasCIFF将用户分类与级联预测任务无缝整合至一个统一框架中，从而提取对所有任务均有用的共同特征，这一策略基于多任务学习的原理。