Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future.

翻译：图表示学习旨在将高维稀疏的图结构数据有效编码为低维稠密向量，这是机器学习与数据挖掘等多个领域广泛研究的基础任务。经典图嵌入方法遵循基本思想：图中互连节点的嵌入向量能够保持相对较近的距离，从而保留节点间的结构信息。然而，这种方法存在次优性，原因在于：(i)传统方法模型容量有限，限制了学习性能；(ii)现有技术通常依赖无监督学习策略，未能与最新学习范式有效结合；(iii)表示学习与下游任务相互依赖，需要协同提升。随着深度学习的显著成功，深度图表示学习相较于浅层（传统）方法展现出巨大潜力和优势，过去十年间涌现了大量深度图表示学习技术，特别是图神经网络。本综述通过提出针对现有前沿文献的新分类体系，对当前深度图表示学习算法进行了全面梳理。具体而言，我们系统总结了图表示学习的核心组成部分，并根据图神经网络架构与最新高级学习范式对现有方法进行分类。此外，本综述还介绍了深度图表示学习的实际应用与前景方向。最后，我们提出了新视角并指出了未来值得深入探索的挑战性研究方向。