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地编码成低维度的密集向量，这是一项广泛研究的基本任务，包括机器学习和数据挖掘等领域。传统的图嵌入方法遵循一种基本思想，即图中互相连接的节点的嵌入向量仍然可以保持相对接近的距离，从而保留节点之间的结构信息。然而，由于：(i) 传统方法的模型容量受限，限制了学习性能；(ii) 现有技术通常依赖于无监督学习策略，并不能与最新的学习范式结合起来；(iii) 表示学习与下游任务相互依赖，应该联合增强。随着深度学习的显著进展，深度图表示学习表现出了巨大的潜力和优势，过去十年中已经出现了大量的深度图表示学习技术，特别是图神经网络。 在本综述中，我们通过提出现有最先进文献的新分类法，对当前的深度图表示学习算法进行了全面的综述。具体地，我们系统地总结了图表示学习的基本组成部分，并将现有方法按照图神经网络架构和最新高级学习范式的方式进行分类。此外，本综述还提供了深度图表示学习的实际和有前途的应用。最后但并非最不重要的是，我们阐述了新的视角，并提出了值得未来进一步研究的挑战性方向。