Relational Feature

Relational features, representing relationships between data points or features, are a crucial area of research aiming to improve the accuracy and interpretability of machine learning models. Current work focuses on incorporating relational information into various architectures, including graph neural networks and relational convolutional networks, to capture complex dependencies and hierarchical structures within data. This research is significant because effectively modeling relational information enhances model performance across diverse applications, from anomaly detection in system logs to improving the efficiency of deep learning training and enhancing the explainability of black-box models. The ability to effectively leverage relational features is driving advancements in various fields, including computer vision, knowledge graph completion, and surgical workflow analysis.