Compressive learning scaffolds higher-order network structure to enhance human knowledge acquisition.

Humans naturally seek knowledge, yet integrating vast, fragmented information remains challenging. Traditionally, knowledge acquisition has relied on random walks within network-an unguided and inefficient process. Here, we introduce "compressive learning", a conceptual framework that embeds higher-order structural features-specifically node-degree inhomogeneity-into pre-learning trajectories to scaffold more efficient learning. We show that scale-free networks, owing to their pronounced degree inhomogeneity, are more compressible and more learnable than other network types. Critically, pre-learning paths that highlight this inhomogeneous higher-order structure facilitate subsequent network learning. Magnetoencephalography (MEG) recordings reveal that compressive pre-learning enhances structured neural representations in the dorsal anterior cingulate cortex (ACC). Two-stage computational modeling indicates that compressive learning constructs a network skeleton defined by higher-order structure that efficiently accommodates new inputs. Together, our results highlight the central role of higher-order network structure in human learning and offer a strategic approach to effectively integrating fragmented information into a coherent knowledge framework.