The large-scale structure of semantic networks: statistical analyses and a model for semantic growth

TL;DR

This paper analyzes the large-scale structure of semantic networks, revealing they are small-world and scale-free, and introduces a growth model that explains these properties and their relation to learning and semantic processing.

Contribution

It provides the first comprehensive statistical analysis of semantic networks and proposes a novel growth model that accounts for their complex structure.

Findings

Semantic networks exhibit small-world and scale-free properties.

The proposed growth model replicates observed network statistics.

Regularities are similar to those in other complex natural networks.

Abstract

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.