Graph Kernel

Graph kernels are mathematical functions that quantify the similarity between graphs, enabling their use in machine learning tasks such as classification and regression. Current research focuses on developing more efficient and expressive graph kernels, often incorporating techniques from graph neural networks (GNNs) and leveraging concepts like random walks, spectral features, and hierarchical substructure comparisons to improve accuracy and scalability. These advancements are significant because they enable the application of kernel methods to increasingly complex and large-scale graph datasets, impacting diverse fields like bioinformatics, social network analysis, and materials science.