# Geometry-V-Sub: An Efficient Graph Attention Network Struct Based Model for Node Classification
## 1 Introduction
The general framework of space-based graph convolution network is Message Passing Neural Networks(MPNN) [10], and the framework implemented through this mechanism has strong plasticity, such as Deep Graph Library [20]. Because the number of neighbors of nodes can vary from one to one thousand or more, it is inefficient to obtain the full size of node neighbors, so GraphSage [5] uses sampling to obtain a fixed number of neighbors, therefore, the operation efficiency is improved. Attention mechanism has made many excellent achievements in multitasking since it was proposed [21]. There is no exception in the study of graph structure data, so there are many excellent models and structures. Graph Attention Network (GAT) [12] improves the model's ability to express the graph by splicing nodes as edge features, learning edge features using attention vectors, and finally aggregating the results by implicitly obtaining edge weights. The Gated Attention Network (GAAN) [13] introduced the self-attention mechanism to calculate different weights for each attention. In addition to applying graph attention in space, [PERSON] also proposed a gating mechanism similar to LSTM to control the information flow across graph volumes. The graph neural network represented by GCN and GAT has good results in model structure and experimental effect. If a new model is developed on the basis of it, it will be easier to succeed. However, when we modify the graph neural network represented by GAT, we find that adding parameters does not achieve better results, which makes us analyze the network model. Generally, if we need the model to achieve better results, having a set of good initialization parameters will greatly improve the model. As mentioned in the paper [22], the model has been greatly improved through good initialization methods. The particle swarm optimization technology and its improved version [23; 24] can greatly improve the artificial neural network, and has been successfully used in the field of medical health. Another way to improve the model is to lightweight the neural network. Lightweight can be achieved by quantifying the network model. For example, the article [25] quantifies the convolutional neural network to make it run on portable devices. In the article [26], the sparse matrix operation is accelerated by compressing data and FPGA hardware equipment, which not only optimizes the calculated data from the perspective of quantification, but also greatly reduces the number of instructions that are not directly related to the operation in the general processor operation through hardware acceleration. Similarly, lightweight can be achieved not only through quantification, but also through the structural innovation and improvement of network model. For example, the article [27] optimizes the aggregation of nodes by establishing an orthogonal coordinate system, so as to improve the performance of the network model. In the article [28], by optimizing the structure of convolutional neural network, it has achieved certain results in computer game. Therefore, driven by the above optimization methods, our graph neural network model decides to create a new model for lightweight design. The newly designed lightweight model Geometric Vector Subtraction (Geometric-V-Sub) in this paper uses the attention mechanism in GAT and the inspiration of geometric learning in the article [29] for reference, further enhances the expression ability of graph neural network by exploring the spatial relationships existing in graph data, greatly reduces parameters with small loss of accuracy, and provides a new network model for subsequent research.