维基百科信息流分析揭示了语义空间的无标度结构
Wikipedia information flow analysis reveals the scale-free architecture of the Semantic Space
摘要：
在本文中，我们提取的语义空间的拓扑结构，在其语义接受，测量最大的现代百科全书，维基百科的不同条目之间的语义流，从而创建一个有向复杂的网络语义流。值得注意的是，在渗透阈值的语义空间的特点是无标度行为在不同层次的复杂性，这涉及到广泛的生物，社会和语言学现象的语义空间。特别是，我们发现，集群的大小分布，代表不同的语义领域的大小，是无标度的。此外，所得到的语义空间的拓扑结构是无标度的连接分布，并显示小世界属性。然而，其统计特性不允许通过基于简单乘法过程的生成模型进行经典解释。语义空间的拓扑性质给出了详细的描述和解释后，我们引入了一个随机模型的基于内容的网络，基于复制和突变算法和堆的法律，这是能够捕获的主要统计特性的分析语义空间，包括Zipf的法律的词频分布。
英文摘要：
In this paper we extract the topology of the semantic space in its encyclopedic acception, measuring the semantic flow between the different entries of the largest modern encyclopedia, Wikipedia, and thus creating a directed complex network of semantic flows. Notably at the percolation threshold the semantic space is characterised by scale-free behaviour at different levels of complexity and this relates the semantic space to a wide range of biological, social and linguistics phenomena. In particular we find that the cluster size distribution, representing the size of different semantic areas, is scale-free. Moreover the topology of the resulting semantic space is scale-free in the connectivity distribution and displays small-world properties. However its statistical properties do not allow a classical interpretation via a generative model based on a simple multiplicative process. After giving a detailed description and interpretation of the topological properties of the semantic space, we introduce a stochastic model of content-based network, based on a copy and mutation algorithm and on the Heaps' law, that is able to capture the main statistical properties of the analysed semantic space, including the Zipf's law for the word frequency distribution.
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