# A Graph Neural Network Node Classification Application Model with Enhanced Node Association
## 2 Related Work
### Classification of Graph Neural Network Nodes  
With the rise of deep learning and the development of graph neural networks, the problem of node classification based on graph data has gained widespread application in the real world. Gradually, semi-supervised node classification of graph neural networks has emerged as a primary focus of research. In recent years, graph-based semi-supervised learning has been a popular area of study, where learning can be performed with a small number of labels by exploiting the graph or stream structure of the data. [PERSON] et al. [20] validated the benefits of semi-supervised learning of graph convolutional neural networks, trained the graph convolution using joint training and self-training methods, and achieved good classification results for semi-supervised classification of graph convolutional neural networks with fewer labels, thereby validating their proposed theory. [PERSON] et al. [21] proposed a new deep hierarchical graph convolutional network (H-GCN) for semi-supervised node classification on a common node classification dataset. The proposed method outperformed the then-current state-of-the-art methods and improved the accuracy by 5.9% with a small sample size, which served as a good inspiration for the subsequent researchers. On the study of node classification, [PERSON] et al. [22] introduced the current characteristics of node classification in graph neural networks and proposed a node embedding enhancement model along with a comprehensive analysis of the graph structure of graph neural networks. By introducing the DSM model and mining the concealed connections between nodes, extensive experiments were conducted on a public dataset, and the results demonstrated that the proposed model was superior to the conventional model. At this juncture, [PERSON] et al. [23] proposed an integrated model of Bagging to address the issue of unbalanced classification of graph neural network nodes. The primary classifier of the model is a graph convolutional neural network, and majority voting is used to complete the integration. Extensive experiments demonstrate that the model is capable of classifying nodes with an imbalance. [PERSON] et al. [24] combined graph neural networks and natural language processing flawlessly to study an RF-EMF model of radio frequency electromagnetic field in order to complete the classification of scientific literature. They obtained promising experimental results and demonstrated the future applicability of the technology to text classification. [PERSON] et al. [25] discovered that graph edge features contain important information, and in this paper, a new framework for partial graph neural networks is developed to enable them to conduct focused computations on graph edge features. In addition to graph node classification of multiple citation networks, the new model is also applied to full graph classification and regression of multiple molecular datasets. It is shown that the proposed model outperforms the then-advanced graph neural network classification models, demonstrating the significance of edge features in graph neural networks. [PERSON] et al. [26] propose a novel model for node and edge feature learning in graph neural networks based on a hierarchical two-layer attention mechanism. They note that most of the state-of-the-art graph learning methods to date only focus on node features, ignoring rich relationship information contained in edge features. The authors demonstrate that node and edge embeddings can mutually enhance each other by training the model on different datasets and validating its feasibility. CensNet, the edge-node switching convolutional graph neural network, was proposed by [PERSON] et al. [27] for semi-supervised classification and regression of graph-structured data with node and edge features. CensNet is a general framework for graph embedding that embeds nodes and edges within a latent feature space.