Graphs face challenges when dealing with massive datasets. They are essential tools for modeling interconnected data and often become computationally expensive. Graph embedding techniques, on the other hand, provide an efficient approach. By projecting complex graphs into a lower-dimensional space, these techniques simplify the analysis and processing of large-scale graphs. By transforming graphs into vectors, it simplifies the analysis and processing of large-scale datasets. Several approaches, such as GraphSAGE, Node2Vec, and FastRP, offer efficient methods for generating graph embeddings. By storing embeddings as node properties, it is possible to compare different embedding techniques and evaluate their effectiveness for specific tasks. This flexibilityallows for dynamic updates to embeddings and facilitates experimentation with different approaches. By analyzing these embeddings, one can extract valuable insights into the relationships between nodes and their similarities within the embedding space

翻译：图在处理大规模数据集时面临挑战。作为建模互联数据的重要工具，图的计算成本通常较高。而图嵌入技术提供了一种高效方法。通过将复杂图投影到低维空间，这些技术简化了大规模图的分析与处理。将图转换为向量形式，能够简化大规模数据集的分析处理流程。GraphSAGE、Node2Vec、FastRP 等多种方法为生成图嵌入提供了高效途径。通过将嵌入存储为节点属性，可以比较不同嵌入技术并评估其在特定任务中的有效性。这种灵活性支持嵌入的动态更新，并便于对不同方法进行实验验证。通过分析这些嵌入，能够深入理解节点间关系及其在嵌入空间中的相似性特征。