Affiliations:
School of Economics & Management, Taiyuan University of Science and Technology, Taiyuan, Shanxi, China
Correspondence:
[*]
Corresponding author. Hai-Ling Guan, School of Economics & Management, Taiyuan University of Science and
Technology, Taiyuan, 030024 Shanxi, China. Tel./Fax: +86 18635136992; E-mail: [email protected].
Abstract: This paper proposed a novel method to analyze the regional collaborative innovation evolution under the perspective of complex network. To clearly describe the complex network evolution problem, the concept of 1) “graph with the characteristic of dynamic” and 2) “network evolution” are provided in advance. Afterwards, we illustrate the internal structure of the regional collaborative innovation system, which contains several elements, such as universities, research institutes, enterprises, intermediary organizations, financial institutions, government and so on. Therefore, we can see that the regional collaborative innovation system can be regarded as a complex system. Furthermore, when the elements in the regional collaborative innovation system can coordinate with each other well, it will be in good working order. In our proposed regional collaborative innovation evolution algorithm, to reduce the computation cost, we suppose that each node in the complex network can only connect to less than a specific nunber of neighbors. Particularly, for a given node, the maximum number of its neighbors is determined by node importance. Afterwards, the regional collaborative innovation evolution can be analyzed by the probability desity function which is calculated by the node importance and node connectivity. Finally, a case study is conducted to make performance evaluation, and we collect the raw dataset from five main sources in 2008–2013. Experimetal results demonstrate that the proposed algortihm can effectively analyze the regional collaborative innovation evolution. Moreover, utilizing the experimental results several suggestions are proposed for government.
