Modeling the average shortest path length in growth of word-adjacency networks

Abstract

We investigate properties of evolving linguistic networks defined by the word-adjacency relation. Such networks belong to the category of networks with accelerated growth but their shortest path length appears to reveal the network size dependence of different functional form than the ones known so far. We thus compare the networks created from literary texts with their artificial substitutes based on different variants of the Dorogovtsev-Mendes model and observe that none of them is able to properly simulate the novel asymptotics of the shortest path length. Then, we identify the local chain-like linear growth induced by grammar and style as a missing element in this model and extend it by incorporating such effects. It is in this way that a satisfactory agreement with the empirical result is obtained.

arXiv commentAccepted for publication in Physical Review E

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Cited by in corpus (11)

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• Complex systems approach to natural language
• Linguistic data mining with complex networks: a stylometric-oriented approach
• In narrative texts punctuation marks obey the same statistics as words
• Representation of texts as complex networks: a mesoscopic approach
• Predicting language diversity with complex network
• Diversity in Valuing Social Contact and Risk Tolerance Lead to the Emergence of Homophily in Populations Facing Infectious Threats
• Text characterization based on recurrence networks
• Hypothesis testing for populations of networks
• Average shortest-path length in word-adjacency networks: Chinese versus English