Multi Label Node Classification

Multi-label node classification focuses on assigning multiple labels to nodes within a graph, a more realistic scenario than the commonly studied multi-class problem. Current research emphasizes developing improved Graph Neural Network (GNN) architectures and addressing the scarcity of suitable datasets by creating new benchmarks and synthetic data generators. This area is significant because accurate multi-label node classification has broad applications, impacting fields like biology and scheduling, where nodes often possess multiple characteristics or functionalities. Ongoing efforts focus on refining GNNs, developing more appropriate evaluation metrics, and understanding the influence of graph structure on model performance.