Small-World Networks and Fundamental Graph Metrics
Networks across domains—neuronal circuits, brain areas, gene-regulatory systems, and social graphs—can be analyzed using graph theory, where nodes represent entities and edges represent interactions or connections.
Watts–Strogatz Model and the Notion of Small-Worldness
Duncan Watts and Steven Strogatz proposed a simple generative model to bridge the gap between regular lattices and random graphs, capturing how a few random shortcuts can transform network behavior.
Brain Hubs and Heavy-Tailed Degree Distributions
Real brain networks—ranging from the full connectome of C. elegans to human structural and functional connectomes—contain hub nodes with far more connections than typical nodes.
Computational Benefits of Small-World Wiring in the Brain
Biological nervous systems must solve three simultaneous engineering problems: enabling specialized local computation, supporting rapid global integration, and remaining robust and energy-efficient despite unreliable components and wiring constraints.