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The Measures of Rank or Status: A Reformulation and Reinterpretation

机译:等级或地位的度量标准:重新表述和重新解释

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This article investigates methods in social network analysis to identify the most important or the most prominent actors in a social network by ranking them appropriately. Although much has been done since Seeley's (194926. Seeley , J. R. ( 1949 ). The net of reciprocal influence: A problem in treating sociometric data . Canadian Journal of Psychology , 3 , 234 - 240 .[CrossRef]View all references) seminal work, several questions remain: How does scaling an adjacency matrix of a social network affect its eigenvalues and their corresponding eigenvectors? How can the differences between the left and the right eigenvectors be reconciled? Do both depict the two sides of the same coin? Can they be “merged” objectively to yield a single eigenvector, and how? Under what conditions does an adjacency matrix characterize, as a first-order approximation, the dynamics of its corresponding social network? Most importantly, how can a general dynamic model be derived to make clear the interrelationships of existing models?In answering these questions, we derive a general model for the evolving rank (which we define as a general measure of prominence) of a (fixed) set of actors in a social network from a system of nonlinear equations; show that, under a rank equivalence condition, an adjacency matrix characterizes the dynamics of its corresponding social network as a first-order approximation; put some existing models within a single framework; and give a solution to the vexing problem of specifying the reference values of the rank.View full textDownload full textKeywordsBoolean matrix, equivalence condition, matrix scaling, measures, models, rank of the receiver, rank of the sender, rank prestigeRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/0022250X.2011.556763
机译:本文研究了社交网络分析中的方法,以通过适当地对它们进行排名来识别社交网络中最重要或最杰出的参与者。尽管自从Seeley(194926. Seeley,JR(1949)。相互影响的网络:处理社会计量学数据以来,已经做了很多工作,加拿大心理学杂志,第3卷,第234-240页。 ,还有几个问题:缩放社交网络的邻接矩阵如何影响其特征值及其对应的特征向量?左本征向量和右本征向量之间的差异如何调和?两者都描绘了同一枚硬币的两个面吗?可以客观地“合并”它们以产生单个特征向量吗?如何?在什么条件下,邻接矩阵将其相应社交网络的动态特征(作为一阶近似)表征为一阶近似?最重要的是,如何得出一般动态模型以阐明现有模型之间的相互关系?在回答这些问题时,我们得出了(固定)演化等级(我们定义为突出程度的一般度量)的一般模型。社会网络中一系列非线性方程组的参与者;表明,在等级等价条件下,邻接矩阵将其相应社交网络的动态特征描述为一阶近似值;将一些现有模型放在一个框架中;并给出了解决指定等级的参考值这一棘手问题的方法。查看全文下载全文关键字布尔矩阵,等价条件,矩阵缩放,度量,模型,接收者的等级,发送者的等级,等级prestigeRelated var addthis_config = { ui_cobrand:“ Taylor&Francis Online”,servicescompact:“ citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,更多”,发布:“ ra-4dff56cd6bb1830b”};添加到候选列表链接永久链接http://dx.doi.org/10.1080/0022250X.2011.556763

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