graph neural networks

The article discusses the problem of instability of test selectors in the process of automated testing of web applications. It raises the issue of selectors’ adaptability to changes in the DOM structure, which is critically important in the development of modern dynamic web interfaces. A comparative analysis of three approaches to selector generation is conducted: manual (via Chrome DevTools), semi-automated (using DevTools), and automated using graph neural networks (GNN).

This paper presents a method for predicting delivery delays and optimizing routes in logistics systems using Graph Neural Networks (GNNs). Modern logistics networks face numerous challenges due to unpredictable delays caused by dynamic traffic conditions, weather events, vehicle malfunctions, and other external factors. Traditional machine learning methods, such as regression models or decision trees, often prove inadequate in modeling such complex spatiotemporal dependencies inherent in logistical environments.