  Science and technology are becoming increasingly collaborative. This paper
aims to explore the factors and mechanisms that impact the dynamic changes of
collaborative innovation networks. We consider both collaborative interactions
of organizations and their knowledge element exchanges to reveal how social and
knowledge network embeddedness affects the collaboration dynamics. Knowledge
elements are extracted to present the core concepts of scientific and technical
information, overcoming the limitations of using predefined categorizations
such as IPC when representing the content. Based on multiple collaboration and
knowledge networks, we then conduct a longitudinal analysis and apply a
stochastic actor-oriented model (SAOM) to model network dynamics over different
periods. The influence of network features and structures, individual node
characteristics, and various dimensions of proximity on collaboration dynamics
is tested and analyzed.
