The Ubiquity of Small-World Networks

By Qawi K. Telesford, Karen E. Joyce, Satoru Hayasaka, Jonathan H. Burdette, Paul J. Laurienti. Brain Connectivity, 2011

462 citations recorded by the source corpus.

Small-world networks, according to Watts and Strogatz, are a class of networks that are “highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.” These characteristics result in networks with unique properties of regional specialization with efficient information transfer. Social networks are intuitive examples of this organization, in which cliques or clusters of friends being interconnected but each person is really only five or six people away from anyone else. Although this qualitative definition has prevailed in network science theory, in application, the standard quantitative application is to compare path length (a surrogate measure of distributed processing) and clustering (a surrogate measure of regional specialization) to an equivalent random network. It is demonstrated here that comparing network clustering to that of a random network can result in aberrant findings and that networks once thought to exhibit small-world properties may not. We propose a new small-world metric, ω (omega), which compares network clustering to an equivalent lattice network and path length to a random network, as Watts and Strogatz originally described. Example networks are presented that would be interpreted as small-world when clustering is compared to a random network but are not small-world according to ω. These findings have important implications in network science because small-world networks have unique topological properties, and it is critical to accurately distinguish them from networks without simultaneous high clustering and short path length.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8c521b75bedb4da6f23e8841e03924fbd92f4f63

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In Experimental and Cognitive Psychology

  1. D. Bassett
  2. E. Bullmore
  3. Olaf Sporns
  4. Yong He
  5. Martijn P. van den Heuvel
  6. Jinhui Wang
  7. Cornelis J. Stam
  8. Alan C. Evans

In Statistical and Nonlinear Physics

  1. D. Bassett
  2. E. Bullmore
  3. Alan C. Evans
  4. Olaf Sporns
  5. Yong He
  6. Jinhui Wang
  7. Sophie Achard

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