Multi-label learning addresses classification tasks in which each instance may be associated with multiple, non-exclusive labels. Unlike traditional single-label approaches, multi-label methods must ...

Graph classification seeks to assign labels to entire graphs by extracting and learning from their structural patterns. Early approaches relied on graph kernels, which measure similarity by counting ...