Geometry-V-Sub: An Efficient Graph Attention Network Struct Based Model for Node Classification

Abstract

With the development of deep learning and graph deep learning, the network structure is more and more complex, and the parameters in the network model and the computing resources and storage resources required are increasing. The lightweight design and optimization of the network structure is conducive to reducing the required computing resources and storage resources, reducing the requirements of the network model on the computing environment, increasing its scope of application, reducing the consumption of energy in computing, and is conducive to environmental protection. The contribution of this paper is that Geometry-V-Sub is a graph learning structure based on spatial geometry, which can greatly reduce the parameter requirements and only lose a little accuracy. The number of parameters is only 13.05–16.26% of baseline model, and the accuracy of Cora, Citeseer and PubMed is max to 80.4%, 68% and 81.8%, respectively. When the number of parameters is only 12.01% of baseline model, F1 score is max to 98.4.