Collective dynamics of 'small-world' networks
Small-world networks combine high clustering (your neighbors know each other) with short path lengths (few hops to reach anyone)
Landmark paper that revolutionized network science by discovering that most real-world networks - from social connections to neural circuits to power grids - exhibit 'small-world' properties: high local clustering combined with short path lengths. This explained the paradox of how networks can be both tightly clustered locally and globally interconnected.
The paper's analysis of C. elegans neural network, power grid, and film actor collaborations provided the foundation for understanding how topology shapes function across biological and organizational systems. With over 41,000 citations, it established network topology as a field and provided the mathematical framework (the Watts-Strogatz model) for designing efficient networks.
Key Findings from Watts & Strogatz (1998)
- Small-world networks combine high clustering (your neighbors know each other) with short path lengths (few hops to reach anyone)
- Random rewiring of just 1-10% of connections transforms regular lattices into small-world networks
- C. elegans neural network exhibits small-world properties with clustering coefficient ~0.28
- Small-world topology enables both functional specialization (high clustering) and rapid information integration (short paths)