Inductive Node

Inductive node classification aims to train machine learning models on one or more graphs and then apply them to predict node labels on entirely new, unseen graphs. Current research focuses on developing novel architectures, such as GraphAny and Structured Proxy Networks, that improve generalization capabilities beyond traditional graph neural networks (GNNs) by incorporating techniques like attention mechanisms and kernel regression. These advancements address the computational challenges of handling large graphs and improve the efficiency and accuracy of inductive node classification, impacting various fields including chemical compound analysis and process discovery.