The Use of Social Network Analysis in Knowledge Diffusion Research from Patent Data

The roles of sharing, transfer, and public funding in nanotechnology knowledge‐diffusion networks

“…Often, these individual attributes have been combined with structural properties of the networks to discover various knowledge‐diffusion patterns. For example, Hsueh and Wang () analyzed patents in the liquid crystal display (LCD) field and identified key institutions and countries that were associated with nodes of high degree centrality. Gao et al.…”

“…Previous research has found that intrinsic motivation to exchange knowledge is another antecedent of knowledge flows (Ko, Kirsch, & King, ; Lin, ). When the network ties represent knowledge‐transfer processes from cited inventors to citing ones (i.e., in the opposite direction of citations), inventors with high out‐degree centrality are associated with high citation counts, and hence a high citation authority (Hsueh & Wang, ; Huang et al., , ). These high authority inventors should have high intrinsic motivations to collaborate with each other to increase the chance of their innovation to be patented.…”

“…They have been found to affect the knowledge flows handled by the node. Examples include regional areas of inventors (Gao et al, 2011), institutions through which the patents are filed (Hsueh & Wang, 2009), patents' relevant technology fields (Cho & Shih, 2011;Li et al, 2007), and availability of public funding to inventors (Huang et al, 2005(Huang et al, , 2006. Often, these individual attributes have been combined with structural properties of the networks to discover various knowledge-diffusion patterns.…”

“…Often, these individual attributes have been combined with structural properties of the networks to discover various knowledge-diffusion patterns. For example, Hsueh and Wang (2009) analyzed patents in the liquid crystal display (LCD) field and identified key institutions and countries that were associated with nodes of high degree centrality. Gao et al (2011) combined the degree of centrality and geographic information of patents and concluded that the most active knowledge exchanges occurred among the most advanced provinces in China from 1985 to 2007.…”

“…For example, based on US Patent Classification Codes and citation counts, important technology fields were identified in knowledge-diffusion networks (Cho & Shih, 2011;Hsueh & Wang, 2009;Li et al, 2007). A similar approach was used to identify key countries and institutions (Hsueh & Wang, 2009;Li et al, 2007) or geographical areas (Gao et al, 2011). In addition to citation counts, several other network characterization techniques have been used to evaluate the importance of nodes.…”