[论文解读] A Comprehensive Survey on Deep Graph Representation Learning

Wei Ju, Zheng Fang+14 more|arXiv (Cornell University)|Apr 11, 2023

Advanced Graph Neural Networks参考文献 563被引用 14

一句话总结

对深度图表示学习的系统分类与全面综述，涵盖 GNN 架构、学习范式与应用。

ABSTRACT

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.

研究动机与目标

• 按 GNN 架构和学习范式组织的深度图表示学习方法的分类法。
• 总结每个类别中的基本组成要素和代表性算法。
• 强调实际应用并讨论挑战、局限性和未来方向。

提出的方法

• 按图神经网络架构对图表示学习方法进行分类（图卷积、图核神经网络、图池化、图变换器）。
• 考察学习范式，包括有监督/半监督、自监督和图结构学习。
• 讨论代表性算法并在每个类别中总结它们的特征。
• 介绍在社会分析、分子性质预测与生成、推荐系统和交通分析中的应用。
• 提供对未来方向和未解决挑战的见解。

实验结果

研究问题

• RQ1 用于深度图表示学习的主要 GNN 架构及其关键操作有哪些？
• RQ2 不同学习范式（有监督/半监督、自监督、结构学习）如何影响图表示？
• RQ3 当前哪些应用最受益于深度图表示，以及仍存在哪些挑战？
• RQ4 哪些未来方向和未解决的问题最有前景以推动深度图表示学习？

主要发现

• 基于 GNN 架构和学习范式提供了一种新的深度图表示学习方法分类。
• 提供各分支中基本组成要素和代表性算法的全面综述。
• 讨论了在社会分析、分子性质预测与生成、推荐系统和交通分析等方面的有潜力的应用。
• 指出了局限性以及未来研究的挑战性方向。