Data, algorithms, and arithmetic power are the three foundational conditions for deep learning to be effective in the application domain. Data is the focus for developing deep learning algorithms. In practical engineering applications, some data are affected by the conditions under which more data cannot be obtained or the cost of obtaining data is too high, resulting in smaller data sets (generally several hundred to several thousand) and data sizes that are far smaller than the size of large data sets (tens of thousands). The above two methods are based on the original dataset to generate, in the case of insufficient data volume of the original data may not reflect all the real environment, such as the real environment of the light, silhouette and other information, if the amount of data is not enough, it is difficult to use a simple transformation or neural network generative model to generate the required data. The research in this paper firstly analyses the key points of the data enhancement technology of graph neural network, and at the same time introduces the composition foundation of graph neural network in depth, on the basis of which the data enhancement technology of graph neural network is optimized and analysed.

翻译：数据、算法与算力是深度学习在应用领域取得成效的三大基础条件，其中数据是发展深度学习算法的核心。在实际工程应用中，部分数据受限于无法获取更多数据或数据获取成本过高的条件，导致数据集规模较小（通常为数百至数千条），其数据量远小于大型数据集（数万条以上）的规模。上述两种方法均基于原始数据集进行生成，在原始数据量不足的情况下可能无法反映真实环境的全部特征，例如真实环境中的光照、轮廓等信息，若数据量不足则难以通过简单的变换或神经网络生成模型来产生所需数据。本文研究首先分析了图神经网络数据增强技术的要点，同时深入介绍了图神经网络的构成基础，并在此基础上对图神经网络数据增强技术进行了优化与分析。