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-ofacquisition, usage frequency) on behavioral performance in semantic processing tasks. Network structures provide intuitive and useful representations for modeling semantic knowledge and inference. Within the paradigm of semantic network models, we can ask at least three distinct kinds of questions. The first type of question concerns structure and knowledge: to what extent can the organization of human semantic knowledge be explained in terms of general structural principles that characterize the connectivity of semantic networks? The second type concerns process and performance: to what extent can human performance in semantic processing tasks be explained in terms of general processes operating on semantic networks? A third type of question concerns the interactions of structure and process: to what extent do the processes of semantic retrieval and search exploit the general structural features of semantic networks, and to what extent do those structural features reflect general processes of semantic acquisition or development? The earliest work on semantic networks attempted to confront these questions in an integrated fashion. Collins and Quillian (1969) suggested that concepts are represented as nodes in a tree-structured hierarchy, with connections determined by classinclusion relations (Figure 1 ). Additional nodes for characteristic attributes or predicates are linked to the most general level of the hierarchy to which they apply. A tree-structured hierarchy provides a particularly economical system for representing default knowledge about categories, but it places strong constraints on the possible extensions of predicates -essentially, on the kinds of knowledge that are possible (Keil, 1979; Sommers, 1971) . Collins and Quillian proposed algorithms for efficiently searching these inheritance hierarchies to retrieve or verify facts such as "robins have wings", and they showed that reaction times of human subjects often seemed to match the qualitative predictions of this model. However, notwithstanding the elegance of this picture, it has severe limitations as a general model of semantic structure. Inheritance hierarchies are clearly appropriate only for certain taxonomically organized concepts, such as classes of animals or other natural kinds. Even in those ideal cases, a strict inheritance structure seems not to apply except for the most typical members of the hierarchy
In REF it was shown that several types of semantic networks have a small-world structure with sparse connectivity.
The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth
Cognitive Science
Physics Computer Science Medicine