The knowledge-base evolution in biotechnology: a social network analysis

This paper applies the methodological tools typical of social network analysis (SNA) within an evolutionary framework, to investigate the knowledge-base (KB) dynamics of the biotechnology sector. Knowledge is here considered a collective good represented as a co-relational and a retrieval-interpretative structure. The internal structure of knowledge is described as a network, the nodes of which are small units within traces of knowledge, such as patent documents, connected by links determined by their joint utilization. We used measures referring to the network (like density) and to its nodes (like degree, closeness and betweenness centrality) to provide a synthetic description of the structure of the KB and of its evolution over time. Eventually, we compared such measures with more established properties of the KB calculated on the basis of co-occurrences of technological classes within patent documents. Empirical results show the existence of interesting and meaningful relationships across the different measures, providing support for the use of SNA to study the evolution of the KBs of industrial sectors and their lifecycles.