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雷达卡
摘要翻译：
我们最近对一个全国性生产网络的研究发现了一个社区结构，即企业如何通过供应商-客户链接连接成紧密结合的群体，群体内密度高，群体间连通性低。在这里，我们提出了一种基于物理类比的图形布局来可视化社区结构的方法。该布局可在实际计算时间内计算，并可通过为天体物理N体模拟开发的GRAPE（重力管道）专用装置进行加速。通过将该方法应用到制造业领域，证明了该方法成功地以分层的方式识别出了由十分之一个节点和五十万条边组成的社区。此外，我们还讨论了该方法的几个局限性，并提出了一种可能的方法来避免所有这些问题。
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英文标题：
《Visualizing a large-scale structure of production network by N-body
simulation》
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作者：
Yoshi Fujiwara
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最新提交年份：
2009
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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：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
Our recent study of a nation-wide production network uncovered a community structure, namely how firms are connected by supplier-customer links into tightly-knit groups with high density in intra-groups and with lower connectivity in inter-groups. Here we propose a method to visualize the community structure by a graph layout based on a physical analogy. The layout can be calculated in a practical computation-time and is possible to be accelerated by a special-purpose device of GRAPE (gravity pipeline) developed for astrophysical N-body simulation. We show that the method successfully identifies the communities in a hierarchical way by applying it to the manufacturing sector comprising tenth million nodes and a half million edges. In addition, we discuss several limitations of this method, and propose a possible way to avoid all those problems.
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PDF链接：
https://arxiv.org/pdf/0901.2381
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