Spatial and temporal complexity of scientific knowledge network and technological knowledge network on China's urban scale

With the rise of the knowledge-based economy in the 1980 s, knowledge(including code and tacit knowledge) as the backbone of innovation has become a key factor affecting production process. Cities have gathered not only a large number of professionals, universities and research institutions, but also a great many producers and consumers, which provides the premise for the innovation actions. City's knowledge storage and its position in the regional knowledge network play an important role in comprehensive competitiveness. Published papers and patents are main outcomes of innovation, which are used to evaluate the urban innovation capability. Moreover, co- publications and co- patents are not only the form of knowledge spillover, but also the key indicators to measure regional innovation. Taking the co-publication and co-patent in the field of biotechnology in China during 2000-2009 as the original data, we built scientific knowledge network(SKN) and technological knowledge network(TKN)between cities. From the perspective of complex networks and geospatial analysis, we explored the temporal and spatial complexity of knowledge spillovers combining the indicators of whole network structure, ego network, power- law, hubs and so on. The results show that: firstly, the nodes degree distribution of SKN and TKN is consistent with the power- law distribution,which means that the both networks not only have a scale- free network structure, but also present a preferential attachment rule when the cities choose the cooperation partner. Secondly,central cities have an obvious hierarchical structure, and are featured by a big scattered and small gathering spatial pattern in SKN, while the TKN is not showing this feature. From the view of central city ego network, the cooperation develops between the coastal capital cities at first, and then turns to inter- regional cooperation, such as Yangtze River Delta, Pearl River Delta, and inter- regional knowledge spillovers is obvious in SKN. The central cities and its partners are still in the coastal city instead of western provincial capitals, and inter- regional knowledge spillovers are not significant in TKN. Thirdly, the temporal evolution of central cities and its ego- network presents hierarchical diffusion and contagious diffusion, and conforms to law of grades process in SKN. The TKN is dominated by hierarchical diffusion.Finally, this study draws conclusions on the temporal and spatial complexity of innovation network, which has a positive impact on quantifying spatial knowledge spillovers and measuring its space- time evolution. Besides, the results clarify the status of each city in innovation networks, which provides a new perspective for the cities to formulate innovative policies.