Research Status of Graph Embedding in Graph Neural Networks
DOI:
https://doi.org/10.6919/ICJE.202507_11(7).0028Keywords:
Graph Embedding; GNNs; Graph Classification; Graph Clustering.Abstract
Graph neural networks (GNNS) is a powerful machine learning tool for processing various graph structure data. Graph embedding is one of the key problems in GNNS. The core of this technology is to map the nodes in the graph structure to the low dimensional space, and usually retain some key information of the nodes in the original graph. In order to capture the structure and attribute relationship between nodes, so as to provide high-quality feature representation for various downstream tasks. Many advanced graph embedding algorithms have been proposed and widely used in various fields. In addition, how to combine graph embedding with other machine learning tasks, such as graph classification, graph clustering and link prediction, is also an important research direction. This paper summarizes and compares the related representative technologies involved by combing the research and development context of graph embedding, effectively filling the gap in the application review of related technologies, and providing reference for researchers on related issues.
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