英文标题：
《A GDP-driven model for the binary and weighted structure of the
International Trade Network》
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作者：
Assaf Almog, Tiziano Squartini, Diego Garlaschelli
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最新提交年份：
2014
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英文摘要：
Recent events such as the global financial crisis have renewed the interest in the topic of economic networks. One of the main channels of shock propagation among countries is the International Trade Network (ITN). Two important models for the ITN structure, the classical gravity model of trade (more popular among economists) and the fitness model (more popular among networks scientists), are both limited to the characterization of only one representation of the ITN. The gravity model satisfactorily predicts the volume of trade between connected countries, but cannot reproduce the observed missing links (i.e. the topology). On the other hand, the fitness model can successfully replicate the topology of the ITN, but cannot predict the volumes. This paper tries to make an important step forward in the unification of those two frameworks, by proposing a new GDP-driven model which can simultaneously reproduce the binary and the weighted properties of the ITN. Specifically, we adopt a maximum-entropy approach where both the degree and the strength of each node is preserved. We then identify strong nonlinear relationships between the GDP and the parameters of the model. This ultimately results in a weighted generalization of the fitness model of trade, where the GDP plays the role of a `macroeconomic fitness\' shaping the binary and the weighted structure of the ITN simultaneously. Our model mathematically highlights an important asymmetry in the role of binary and weighted network properties, namely the fact that binary properties can be inferred without the knowledge of weighted ones, while the opposite is not true.
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中文摘要：
最近发生的全球金融危机等事件重新激发了人们对经济网络话题的兴趣。国家间冲击传播的主要渠道之一是国际贸易网络（ITN）。ITN结构的两个重要模型，经典的贸易引力模型（在经济学家中更受欢迎）和适应度模型（在网络科学家中更受欢迎），都仅限于描述ITN的一个代表。引力模型令人满意地预测了相连国家之间的贸易量，但无法重现观察到的缺失环节（即拓扑结构）。另一方面，适应度模型可以成功地复制ITN的拓扑结构，但无法预测容量。本文试图在统一这两个框架方面迈出重要一步，提出了一个新的GDP驱动模型，该模型可以同时重现ITN的二元和加权特性。具体来说，我们采用了最大熵方法，其中每个节点的度和强度都保持不变。然后，我们确定了GDP与模型参数之间的强非线性关系。这最终导致贸易适应度模型的加权推广，其中GDP扮演着“宏观经济适应度”的角色，同时塑造了ITN的二元结构和加权结构。我们的模型在数学上强调了二元和加权网络性质的一个重要不对称性，即二元性质可以在不了解加权性质的情况下推断，而反之则不成立。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：General Finance 一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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一级分类：Physics 物理学
二级分类：Physics and Society 物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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