Cascade prediction helps us uncover the basic mechanisms that govern collective human behavior in networks, and it also is very important in extensive other applications, such as viral marketing, online advertising, and recommender systems. However, it is not trivial to make predictions due to the myriad factors that influence a user’s decision to reshare content. This paper presents a novel method for predicting the increment size of the information cascade based on an end-to-end neural network. Learning the representation of a cascade in an end-to-end manner circumvents the difficulties inherent to blue the design of hand-crafted features. An attention mechanism, which consists of the intra-attention and inter-gate module, was designed to obtain and fuse the temporal and structural information learned from the observed period of the cascade. The experiments were performed on two real-world scenarios, i.e., predicting the size of retweet cascades on Twitter and predicting the citation of papers in AMiner. Extensive results demonstrated that our method outperformed the state-of-the-art cascade prediction methods, including both feature-based and generative approaches.

中文翻译：

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注意力神经网络的信息级联预测

级联预测有助于我们发现控制网络中集体人类行为的基本机制，这在广泛的其他应用程序中也非常重要，例如病毒式营销，在线广告和推荐系统。但是，由于影响用户决定转发内容的决定的因素众多，因此进行预测并非易事。本文提出了一种基于端到端神经网络的信息级联增量预测的新方法。以端到端的方式学习级联的表示方法可以避免使手工制作的功能设计变蓝的固有困难。设计了一种由内部注意和门间模块组成的注意机制，以获取并融合从观察到的级联周期中学到的时间和结构信息。实验是在两个真实的场景中进行的，即预测Twitter上转发的级联的大小以及预测AMiner中论文的引用。大量结果表明，我们的方法优于包括基于特征的方法和生成方法在内的最新级联预测方法。