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Social networks and similarity of site assemblages

机译:站点组合的社交网络和相似性

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There have been a number of similarity measures developed in a variety of research domains. Generally, these measures are developed for a specific context and later reused in other contexts and applications, depending on their ease of use and perceived applicability. While there might be statistical reasons to use a particular similarity index, the results of other measures should be taken into account as well, as various similarity measures do not necessarily have similar contextual meaning. Two entities can be similar with respect to a certain similarity criterion but may be distinct in terms of another. Thus, an understanding of the mathematical logic behind a method is crucial to the interpretation of the resulting network of similarities. We review a number of methods from the literature, for constructing similarity networks among disparate entities, regarding their applicability on data from archaeological sites. Formally, given an N X p matrix of N entities with p distinct classes of attributes, how are the entities comparable to each other with respect to the kinds of attributes they share? We distinguish three qualitatively different families of similarity measures for deducing relationships among entities that may meaningfully map onto various distinct social phenomena, such as migration, material acquisition, and movement of goods and skills, among others. Entities can be compared based on: (a) non{uniform weighting of attributes, (b) asymmetric dominance relationships, and (c) rank correlations. We ground the significance and distinction of these classes of measures by giving comparative and contextual examples of selected methods on a case study of archaeological collections pertaining to 1200-1500 CE from the US Southwest region. We attempt to elucidate the differences in outcomes and their meanings when choosing various similarity methods for comparing disparate entities. (C) 2017 Published by Elsevier Ltd.
机译:在许多研究领域中,已经开发了许多相似性度量。通常,这些度量是针对特定的上下文开发的,然后根据其易用性和可感知的适用性在其他上下文和应用程序中重用。虽然使用特定的相似性指数可能有统计上的原因,但也应考虑其他度量的结果,因为各种相似性度量不一定具有相似的上下文含义。两个实体可以在某个相似性标准上相似,但在另一个方面可能不同。因此,理解方法背后的数学逻辑对于解释由此产生的相似网络至关重要。我们回顾了文献中用于在不同实体之间构建相似网络的一些方法,以及它们对考古遗址数据的适用性。形式上,给定一个由N个实体组成的N X p矩阵,其中有p个不同的属性类别,这些实体在共享的属性种类方面如何相互比较?我们区分了三个性质不同的相似性度量族,用于推断实体之间的关系,这些关系可能有意义地映射到各种不同的社会现象,例如移民、物质获取、商品和技能的流动等。实体的比较可以基于:(a)属性的非一致加权,(b)不对称优势关系,以及(c)等级相关性。我们通过对美国西南地区1200-1500 CE考古藏品的案例研究,给出了所选方法的对比和上下文示例,从而确定了这些措施的重要性和区别。我们试图阐明在选择各种相似性方法来比较不同实体时,结果的差异及其意义。(C) 2017年由爱思唯尔有限公司出版。

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