Small-World Networks
Small-world topology enables organizations to achieve both local efficiency (teams work closely together) and global reach (cross-team shortcuts enable coordination).
Connection patterns matter more than individual components. Same people, same resources, different topology - radically different outcomes.
The Watts-Strogatz model (1998) formalizes small-world networks: start with a regular ring lattice where each node connects to nearest neighbors, creating high local clustering but requiring many hops to reach distant nodes. Randomly rewire a fraction p of connections - redirect endpoints to random nodes. At intermediate values (p ~ 0.01-0.1), the network becomes small-world: retains most local structure while shortcuts dramatically collapse path lengths.
Neural networks exhibit small-world topology: C. elegans (302-neuron connectome) shows high clustering coefficient (~0.3-0.5) and short characteristic path lengths. Human brain functional networks exhibit path length L ~3-4. This enables segregation (specialized processing in visual/motor cortex) plus integration (cross-modal awareness). Information reaches all neurons in ~3-4 hops without requiring metabolically expensive dense global connectivity.
Business Application of Small-World Networks
Small-world topology enables organizations to achieve both local efficiency (teams work closely together) and global reach (cross-team shortcuts enable coordination). DHL's logistics network exemplifies this: dense regional hub clusters with intercontinental flight shortcuts achieving 2-4 hop delivery worldwide.
Discovery
Duncan Watts and Steven Strogatz (1998)
Discovered that most real-world networks exhibit small-world properties: high local clustering combined with short path lengths, explaining the 'six degrees of separation' phenomenon