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arXiv 0802.4170physics.soc-phcond-mat.stat-mech
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类向量加权网络演化的簇扩展方法

Cluster Expansion Method for Evolving Weighted Networks Having Vector-like Nodes

Marcel Ausloos, Mircea Gligor

摘要:

将统计力学和凝聚态中已知的簇变分法应用于加权二分网络。通过有限数量的组件,从而服务于定义变量集群的哈密尔顿的分解,被召回。作为一个例子,网络建立的数据表示(4)宏观经济特征,即所谓的$vector$ $components$,15个欧盟国家之间的相关性,作为(功能)节点,进行了讨论。我们表明,统计物理原理,如最大熵准则指向集群,在这里(4)变量相空间:国内生产总值(GDP),最终消费支出(FCE),总资本形成(GCF)和净出口(NEX)。据观察,$maximum$熵对应于这样一个聚类,该聚类$not$明确包括GDP,但仅包括其他(3)“轴”,即消费、投资和贸易组成部分。另一方面,$minimal$熵聚类方案是从必然包括GDP和FCE的耦合获得的。结果证实了直观的经济理论和实践的预期,至少在地理联系。这种技术当然可以应用于社会经济网络物理学中的许多其他情况。

英文摘要:

The Cluster Variation Method known in statistical mechanics and condensed matter is revived for weighted bipartite networks. The decomposition of a Hamiltonian through a finite number of components, whence serving to define variable clusters, is recalled. As an illustration the network built from data representing correlations between (4) macro-economic features, i.e. the so called $vector$ $components$, of 15 EU countries, as (function) nodes, is discussed. We show that statistical physics principles, like the maximum entropy criterion points to clusters, here in a (4) variable phase space: Gross Domestic Product (GDP), Final Consumption Expenditure (FCE), Gross Capital Formation (GCF) and Net Exports (NEX). It is observed that the $maximum$ entropy corresponds to a cluster which does $not$ explicitly include the GDP but only the other (3) ''axes'', i.e. consumption, investment and trade components. On the other hand, the $minimal$ entropy clustering scheme is obtained from a coupling necessarily including GDP and FCE. The results confirm intuitive economic theory and practice expectations at least as regards geographical connexions. The technique can of course be applied to many other cases in the physics of socio-economy networks.

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