Recently, information cascade prediction has attracted increasing interest from researchers, but it is far from being well solved partly due to the three defects of the existing works. First, the existing works often assume an underlying information diffusion model, which is impractical in real world due to the complexity of information diffusion. Second, the existing works often ignore the prediction of the infection order, which also plays an important role in social network analysis. At last, the existing works often depend on the requirement of underlying diffusion networks which are likely unobservable in practice. In this paper, we aim at the prediction of both node infection and infection order without requirement of the knowledge about the underlying diffusion mechanism and the diffusion network, where the challenges are two-fold. The first is what cascading characteristics of nodes should be captured and how to capture them, and the second is that how to model the non-linear features of nodes in information cascades. To address these challenges, we propose a novel model called Deep Collaborative Embedding (DCE) for information cascade prediction, which can capture not only the node structural property but also two kinds of node cascading characteristics. We propose an auto-encoder based collaborative embedding framework to learn the node embeddings with cascade collaboration and node collaboration, in which way the non-linearity of information cascades can be effectively captured. The results of extensive experiments conducted on real-world datasets verify the effectiveness of our approach.

近年来，信息级联预测已引起研究人员的越来越多的兴趣，但是由于现有工作的三个缺陷，其远未得到很好的解决。首先，现有的工作通常会假设一个潜在的信息传播模型，由于信息传播的复杂性，在现实世界中这是不切实际的。其次，现有工作往往忽略了感染顺序的预测，这在社交网络分析中也起着重要作用。最后，现有的工作通常取决于底层扩散网络的要求，而这些在实践中可能是无法观察到的。在本文中，我们的目标是对节点感染和感染顺序进行预测，而无需了解潜在的扩散机制和扩散网络，而挑战是双重的。第一个是应该捕获节点的级联特性以及如何捕获它们，第二个是如何在信息级联中建模节点的非线性特征。为了解决这些挑战，我们提出了一种称为深度协作嵌入（DCE）的新型模型，用于信息级联预测，该模型不仅可以捕获节点结构属性，而且可以捕获两种节点级联特征。我们提出了一种基于自动编码器的协作嵌入框架，以通过级联协作和节点协作来学习节点嵌入，从而可以有效地捕获信息级联的非线性。在真实数据集上进行的广泛实验的结果证明了我们方法的有效性。