Research and Implementation of Data Enhancement Techniques for Graph Neural Networks

TL;DR

This paper analyzes and optimizes data enhancement techniques for graph neural networks, addressing challenges of limited data in practical applications and improving the effectiveness of GNN training.

Contribution

It provides an in-depth analysis of key data enhancement methods for GNNs and proposes optimizations based on the foundational composition of graph neural networks.

Findings

Enhanced data augmentation methods for GNNs.

Improved performance in limited data scenarios.

Deeper understanding of GNN data enhancement techniques.

Abstract

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.