从外部分解，从内部融合：工业贸易网络中的社会结构和行为
Dissortative From the Outside, Assortative From the Inside: Social Structure and Behavior in the Industrial Trade Network
摘要：
一般认为，金融网络中的相邻节点在度相关性方面是负分类的。这一特征将在市场低迷时期（困境时期）发挥重要的“阻尼”作用，因为公司之间的这种连接模式降低了危机自动放大（传播）的机会。在本文中，我们探讨了一个工业企业的贸易网络，其中的节点是供应商或买家，和链接是那些发票，供应商发送给他们的买家，然后去出示给他们的银行贴现。2007年，一家大型意大利银行通过其客户赊销的中介活动收集了该网络。该网络还显示出其他金融网络研究中所看到的dissortative行为。然而，当我们观察公司的信用评级时，我们发现，相互交易的公司具有压倒性的相似性。我们知道我们的数据集缺少很多数据。然而，我们可以使用信息暴露来量化缺失数据的数量，这是一个连接社会结构和行为的变量。此变量是供应商向其银行提交的销售发票与其总销售额的比率。结果显示，一个非平凡的和强大的关系之间的信息暴露和公司的信用评级，表明邻居对公司的评级的影响。该方法为如何重构信息不完全的网络提供了新的思路。
英文摘要：
It is generally accepted that neighboring nodes in financial networks are negatively assorted with respect to the correlation between their degrees. This feature would play an important 'damping' role in the market during downturns (periods of distress) since this connectivity pattern between firms lowers the chances of auto-amplifying (the propagation of) distress. In this paper we explore a trade-network of industrial firms where the nodes are suppliers or buyers, and the links are those invoices that the suppliers send out to their buyers and then go on to present to their bank for discounting. The network was collected by a large Italian bank in 2007, from their intermediation of the sales on credit made by their clients. The network also shows dissortative behavior as seen in other studies on financial networks. However, when looking at the credit rating of the firms, an important attribute internal to each node, we find that firms that trade with one another share overwhelming similarity. We know that much data is missing from our data set. However, we can quantify the amount of missing data using information exposure, a variable that connects social structure and behavior. This variable is a ratio of the sales invoices that a supplier presents to their bank over their total sales. Results reveal a non-trivial and robust relationship between the information exposure and credit rating of a firm, indicating the influence of the neighbors on a firm's rating. This methodology provides a new insight into how to reconstruct a network suffering from incomplete information.
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