论文标题
信息级联分析的调查：模型，预测和最新进展
A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
论文作者
论文摘要
从用户生成的内容（例如微博和科学论文）到在线业务（例如病毒营销和广告），我们日常生活中的数字信息泛滥，提供了前所未有的机会，可以探索和利用信息级联进化的轨迹和结构。大量的学术和工业研究工作旨在更好地了解推动信息传播的机制并量化信息传播的结果。本文介绍了信息受欢迎程度预测方法的全面综述和分类，从特征工程和随机过程，通过图表，到基于深度学习的方法。具体而言，我们首先正式定义了不同类型的信息级联，并总结了现有研究的观点。然后，我们提出了一种分类学，将现有作品分为上述三个主要小组以及每个组的主要子类，并系统地审查尖端的研究工作。最后，我们总结了现有研究工作的利弊，并概述了该领域的公开挑战和机遇。
The deluge of digital information in our daily life -- from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising -- offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes, through graph representation, to deep learning-based approaches. Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.
