2006/05/08 by A. P. Masucci, A. Paolo Masucci, G. J. Rodgers
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Opinion Dynamics and Social Influence #Topological and Geometric Data Analysis #cond-mat.stat-mech #physics.data-an #physics.soc-ph
paper · pdf · doi:10.1103/physreve.74.026102
published as Phys Rev E.74. 026102, 2006 · 9 pages, 8 figures
arxiv created 2006/05/08 · openalex publication_date 2006/08/02 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We investigate the nature of written human language within the framework of complex network theory. In particular, we analyze the topology of Orwell's "1984" focusing on the local properties of the network, such as the properties of the nearest neighbors and the clustering coefficient. We find a composite power law behavior for both the average nearest neighbor's degree and average clustering coefficient as a function of the vertex degree. This implies the existence of different functional classes of vertices. Furthermore, we find that the second order vertex correlations are an essential component of the network architecture. To model our empirical results we extend a previously introduced model for language due to Dorogovtsev and Mendes. We propose an accelerated growing network model that contains three growth mechanisms: linear preferential attachment, local preferential attachment, and the random growth of a predetermined small finite subset of initial vertices. We find that with these elementary stochastic rules we are able to produce a network showing syntacticlike structures.