基于ERG模型的专利引用关系形成影响因素研究

专利引文由于在科技评价过程中具有十分重要的作用，近年来一直是研究的重点。然而，作为专利引文研究理论基石的专利引文关系形成的影响因素问题并没有得到较好的解决。随着网络分析方法的深入，围绕着专利引文网络结构特征的研究出现了大量的研究成果，这些成果都从某种程度上折射出专利引文关系的形成受到了来自属性特征之外关系特征的影响，而现有的以回归方法为基础的统计推断方法难以将这些因素纳入进分析框架中来。本文借鉴指数随机图建模理论框架，将影响专利引用关系形成的若干因素归纳为网络自组织过程，属性特征影响过程与外部情境影响过程等因素，以PATSTAT风能数据为基础，本文根据不同类型的影响过程分别构建了若干独立的过程模型以及综合模型，通过对不同模型参数估计结果以及拟合优度的比较发现：专利的属性特征对于专利引用关系形成的影响被高估了；而引用关系的自组织过程对于专利引用关系的形成产生了更为重要的影响。该研究结论的发现，为下一步改进专利引用关系形成影响因素问题研究指明了方向。

Determinants of patent citation formation based on the ERG model

In recent years, patent citations have play a very important role in the process of S&T evaluation, and have attached much attentions. However, as the foundation of patent citation research, what’s determinants of patent citation formation are not solved satisfactorily. With embedded study, scholars have formed an amount of research outputs on patent citation network. These outputs reflected that the formation of patent citation was influenced by the structure characteristics of patent citation network, However, the current framework of existing statistical inference methods based on logistical regression was failing to incorporate the above factors. This article uses an ERG (exponential random graph) model approach, which is a tie-focused branch consists of models that aims to explain or predict ties and their patterns. Under the framework of patent citation formation, attribute-based processes, node-level covariates processes, and self-organizing network processes are incorporated into ERG models. The usefulness of ERG models is illustrated by an empirical study on the structure of wind energy patent citation network originated from the PATSTAT database. The results show that the self-organized level determinants have a more important impact in explaining formation of patent citation, especially “transitivity closure”, and probably the influence of attributes-based factors were overestimated. At last, this paper discusses the direction for the next step work.