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

Summary 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. We introduce compressive learning, a framework that embeds higher-order structural features—specifically node-degree inhomogeneity—into pre-learning trajectories to scaffold more efficient learning. Across two large-scale experiments, we demonstrate that scale-free networks—due to their pronounced node-degree inhomogeneity—are more compressible and learnable than other network types, and confirm the efficacy of the compressive learning approach. Magnetoencephalography (MEG) recordings reveal that compressive pre-learning enhances structured neural representations in the dorsal anterior cingulate cortex (ACC). A hypergraph-based two-stage model further reveals that compressive learning constructs a network skeleton of hyperedge-defined substructures that more effectively accommodate new inputs. Together, our results highlight the central role of higher-order network structure in human learning and offer a strategic approach to effectively “connect the dots.”