标题： 图深度学习简介 显示英文标题

标题： A Gentle Introduction to Deep Learning for Graphs

摘要： 图数据的自适应处理是一个长期的研究课题，最近已被确立为深度学习社区中一个重要的研究主题。与之相关的研究数量和广度迅速增加，但知识的系统化整理和对早期文献的关注却相对较少。 本文旨在为图的深度学习领域提供一个教程式的介绍。 它更倾向于以一致且逐步的方式介绍主要概念和体系结构方面，而不是详细阐述最新的文献，关于后者，读者可以参考现有的综述文章。 本文从问题的全局视角出发，基于局部和迭代的信息处理方法，提出了图表示学习的一个广义公式。 它介绍了可以组合起来设计新型且有效的图神经模型的基本构建模块。 方法论的阐述辅以对该领域有趣的研究挑战和应用的讨论。 显示英文摘要

摘要： The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This work is designed as a tutorial introduction to the field of deep learning for graphs. It favours a consistent and progressive introduction of the main concepts and architectural aspects over an exposition of the most recent literature, for which the reader is referred to available surveys. The paper takes a top-down view to the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured information processing. It introduces the basic building blocks that can be combined to design novel and effective neural models for graphs. The methodological exposition is complemented by a discussion of interesting research challenges and applications in the field.