Analyses of complex networks of industries and formulations of the growing models

公司之间建立了各种各样的关系。这种关系不应该是独立的，而应该是依赖的。此外，这种关系也会影响到与公司的关系。因此，为了了解企业关系的行为，我研究了以企业为节点，以各种关系为纽带的网络。在这里，我使用了由公司员工撰写的专利和论文的共同作者身份。人们发明了许多方法来分析网络。我从中选择了p模型。P模型是一种概率模型，可以检测到具有概率显著性的结构。在模型的基础上，我发现日本的四个重要产业分别具有不同的结构。例如，有一个结构，一个人专门发表论文或专利。作为另一种分析，我使用了交易和专利合作署名。这些关系比其他变量（如行业或公司规模）之间的联系更为紧密。这是由贝叶斯网络获得的，不能通过一般最小二乘分析获得。

Companies create various kinds of relationships with each other. The relationships must be not independent but dependent. Also, the relationships affect with companies. Therefore, to know the behavior of the relationships of companies, I investigated networks where companies are nodes and various kinds of the relationships are links.Here, I used co-authorships of patents and co-authorships of papers that were written by company workers. Many methods have been invented to analyze networks. I chose p model from them. p model is a probabilistic model that can detect probabilistically significant structures. On the basis of the model, I found four important Japanese industries have different structures respectively. For example, there is a structure where a person publishes papers or patents specifically.As another analysis, I used transactions and patent co-authorships. These relationships are more strongly connected than other variables, such as sectors or scales of companies. This was acquired by Baysian networks and cannot be acquired by ordinally least square analyses.