人类书面语言的网络特性
Network properties of written human language
摘要：
我们在复杂网络理论的框架内研究人类书面语言的本质。特别是，我们分析了奥威尔的\textit{1984}的拓扑结构，侧重于网络的局部属性，如最近邻的属性和聚类系数。我们发现一个复合的幂律行为的平均最近邻的程度和平均聚类系数作为顶点度的函数。这意味着存在不同的功能类的顶点。此外，我们发现，二阶顶点相关性的网络结构的一个重要组成部分。为了模拟我们的实证结果，我们扩展了以前介绍的语言模型由于Dorogovtsev和门德斯。我们提出了一个加速增长的网络模型，它包含三种增长机制：线性优先连接，局部优先连接和随机增长的一个预先确定的小有限子集的初始顶点。我们发现，与这些基本的随机规则，我们能够产生一个网络，显示类似语法的结构。
英文摘要：
We investigate the nature of written human language within the framework of complex network theory. In particular, we analyse the topology of Orwell's \textit{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 pre-determined small finite subset of initial vertices. We find that with these elementary stochastic rules we are able to produce a network showing syntactic-like structures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
