Graph Metric

Graph metrics quantify relationships and structures within graph-structured data, aiming to provide meaningful representations for downstream machine learning tasks. Current research focuses on developing efficient and effective graph metrics, particularly for scenarios with limited or missing node features, leveraging techniques like histogram-based encoding and optimal transport methods, and adapting metrics for various graph types and tasks (e.g., classification, regression, continual learning). These advancements are crucial for improving the performance and interpretability of graph neural networks across diverse applications, including social network analysis, medical image analysis, and natural language processing.