Graph Feature

Graph features are descriptive characteristics extracted from graph-structured data to facilitate machine learning tasks such as graph classification, link prediction, and node classification. Current research emphasizes developing efficient feature extraction methods, including those based on structural properties, graph embeddings (e.g., using Ricci flow or hyperbolic space), and graph neural networks (GNNs) with various architectures like message-passing networks and graph transformers. These advancements improve the accuracy and efficiency of graph-based analyses across diverse applications, ranging from social network analysis and bioinformatics to financial crime detection and drug discovery.