Modeling the average shortest path length in growth of word-adjacency networks
This work addresses a specific issue in linguistic network modeling for researchers in network science and computational linguistics, representing an incremental improvement by extending an existing model.
The study tackled the problem of modeling the average shortest path length in evolving word-adjacency networks from literary texts, finding that existing models failed to capture its novel asymptotics, and by incorporating local chain-like linear growth effects, they achieved satisfactory agreement with empirical results.
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.