Evolution Of Multidimensional Knowledge Network Proximity Perspective

With the coming of era of knowledge economy, knowledge network’s effect on knowledge transfer and innovation is gradually been taken seriously. Studies on factors influencing the evolution of knowledge network’s structure have significance for improving the structure of knowledge network between regions. In recent years, the corelational studies in the west pay close attention to proximity perspective, while the domestic researches focus on model simulation and theory explaining.Bioindustry and nanotechnology industry both belong to knowledge-intensive high-tech industries. With further research, they have increasingly high demand for cooperation. Besides, our country is attaching great importance to the two industries. So studying Biotechnology and nanotechnology knowledge networks has practical significance.In view of this, this paper analyses the evolution of network structure of biotechnology and nanotechnology from the aspects of network topology structure, spatial network, network correlation property and key nodes using co-authorships of Chinese research publications in2000-2010from international ISI database in the field of biotechnology as data sources, combined with social network analysis software UCINET and geographical information system software ArcGis. To further explore the role of multidimensional proximity on knowledge network’s structure evolution, this paper analyses respectively from the three aspects of unique proximity’s effect, geographical effect boundary analysis and QAP regression. It not only finds the geographical boundary, but also reveals the multidimensional proximity’s effect size and their interaction on knowledge network evolution.The main conclusions are as follows:First, the spatial difference in China’s knowledge network structure is gradually shrinking. In the early stages of this study, the difference was very obvious. Cooperations were mainly concentrated in the eastern coastal area. While in later stages, more western cities are involved in the knowledge network, and their cooperation strength with other cities are also increasing. Several western nodes with strong intermediary roles were found in the analysis of hub nodes, these nodes play key roles in narrowing spatial difference and will promote knowledge flow in the national scope.Second, biotechnology and nanotechnology knowledge network are in different stages of development. Biotechnology knowledge network is in a period of rapid growth, while nanotechnology knowledge network is in the mature development stage. The former’s line number and size are in more rapid growth, but its compactness is not enough and degree distribution is uneven. Its robustness mainly depends more on the high degree nodes. The latter’s degree distribution is evener, network density is larger and average path is smaller than former’s. Besides, its small-world property is more significant.Third, China’s knowledge network hierarchy has no distinct difference. Through the analysis of degree-degree correlation, clustering coefficient-degree correlation and network space structure, we find that the top structure of knowledge network has been formed. Although there are also some regional secondary centers, new nodes still tend to connect with the most advanced ones not regional hubs. This results in the secondary structure of network can not develop well.Fourth, geographical proximity’s effect is different in different spatial scales. By constructing the expected value matrix and running Spss regression analysis, this paper finds that geographical effect "boundary" is not immutable:the value is about700-800km in biotechnology, while it gradually rises in nanotechnology. Within the "boundary ", geographical proximity’s role in promoting cooperation is significant, while its effect is not significant outside the "boundary".Fifth, multiple proximity’s effects on the evolution of knowledge network’s structure are different. This study shows that:geographical proximity’s effect is weak and falling, while organizational proximity’s effect is the strongest and rising, social proximity’s effect ranks the second. Besides, the level of geographical distance has a positive regulatory effect between cognitive proximity and scientific cooperation. When the geographical distance is far, in order to compensate for the lack of geographical proximity, nodes tend to cooperate with those who have higher cognitive proximity with themselves. So, government departments should not only make some measures to promote "geographical proximity", but also take into account scientific research units’"organization and social proximity ".