  We present statistical analyses of the large-scale structure of three types
of semantic networks: word associations, WordNet, and Roget's thesaurus. We
show that they have a small-world structure, characterized by sparse
connectivity, short average path-lengths between words, and strong local
clustering. In addition, the distributions of the number of connections follow
power laws that indicate a scale-free pattern of connectivity, with most nodes
having relatively few connections joined together through a small number of
hubs with many connections. These regularities have also been found in certain
other complex natural networks, such as the world wide web, but they are not
consistent with many conventional models of semantic organization, based on
inheritance hierarchies, arbitrarily structured networks, or high-dimensional
vector spaces. We propose that these structures reflect the mechanisms by which
semantic networks grow. We describe a simple model for semantic growth, in
which each new word or concept is connected to an existing network by
differentiating the connectivity pattern of an existing node. This model
generates appropriate small-world statistics and power-law connectivity
distributions, and also suggests one possible mechanistic basis for the effects
of learning history variables (age-of-acquisition, usage frequency) on
behavioral performance in semantic processing tasks.
