Graphs at the Scale of a Population

Network science treats society as a graph: nodes are individuals, edges are relationships, contacts, or communication events. At population scale — millions of nodes, billions of edges — computing centrality, community structure, or epidemic spread requires memory-dense HPC nodes rather than a single workstation. Combined with anonymized population mobility data, this lets researchers model information diffusion, disease spread, and migration patterns with a fidelity that was computationally out of reach a decade ago. Malgukke's large-memory and GPU-accelerated graph architectures are built for exactly this workload.