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A '''small-world network''' is a graph topology in which most nodes are not neighbors of one another, yet the average shortest path between any two nodes is small. This property — high local clustering combined with short global path lengths — was first formalized by Watts and Strogatz in 1998 as an interpolation between regular lattices and random graphs.

Small-world topology appears in [[Neuroscience|neural networks]], social networks, and power grids, suggesting that efficient information transmission and local redundancy are jointly optimized by selection pressures across vastly different systems. The small-world property is closely related to '''[[Navigability in networks|navigability]]''', the ability to find short paths using only local information.

[[Category:Mathematics]] [[Category:Systems]]