论文标题
图表表示方法的调查
A Survey on Graph Representation Learning Methods
论文作者
论文摘要
近年来，图表表示学习一直是一个非常活跃的研究领域。图表学习的目标是生成图表向量，以准确捕获大图的结构和特征。这尤其重要，因为图表向量的质量将影响这些向量在下游任务中的性能，例如节点分类，链接预测和异常检测。提出了许多用于生成有效图表向量的技术。图形表示学习的两个最普遍的类别是图形嵌入方法，而无需使用图神经网（GNN），我们将其表示为基于非GNNN的图形嵌入方法，以及基于图形神经网（GNN）方法。非GNN图嵌入方法基于随机步行，时间点过程和神经网络学习方法等技术。另一方面，基于GNN的方法是在图形数据上应用深度学习。在此调查中，我们提供了这两种类别的概述，并涵盖了静态图和动态图的当前最新方法。最后，我们探索了一些未来工作的开放式研究方向。
Graphs representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate graph representation vectors that capture the structure and features of large graphs accurately. This is especially important because the quality of the graph representation vectors will affect the performance of these vectors in downstream tasks such as node classification, link prediction and anomaly detection. Many techniques are proposed for generating effective graph representation vectors. Two of the most prevalent categories of graph representation learning are graph embedding methods without using graph neural nets (GNN), which we denote as non-GNN based graph embedding methods, and graph neural nets (GNN) based methods. Non-GNN graph embedding methods are based on techniques such as random walks, temporal point processes and neural network learning methods. GNN-based methods, on the other hand, are the application of deep learning on graph data. In this survey, we provide an overview of these two categories and cover the current state-of-the-art methods for both static and dynamic graphs. Finally, we explore some open and ongoing research directions for future work.
