Sentence examples for current centrality from inspiring English sources

In Model 1, the previous centrality has significant positive effects on current centrality (0.479, p < 0.001), H1 is supported.

Therefore we propose hypothesis H1: H1: In a virtual community, the prior centrality of the individual has a positive effect on current centrality.

In a dynamic virtual community, the frequency and originality of individual sharing behavior play incomplete mediating roles between previous and current centrality.

Previous individual centrality in the virtual community will promote original knowledge sharing selection at the current time, while original knowledge sharing will further contribute to the current centrality of the individual in the knowledge network.

At the same time, individual knowledge sharing behavior has an "inertia effect": individual prior status (the degree of node centrality) affects current knowledge sharing behavior, while current knowledge sharing behavior affects current status in the knowledge network, forming an inertial circuit between personal behavior and network status.

Moreover, the individual knowledge sharing behavior has an "inertia effect": individual previous status (the degree of node centrality) affects current knowledge sharing behavior, while current knowledge sharing behavior affects the current status in the knowledge network, forming an inertial circuit between personal behavior and network status.

Even after decades of social network research, the current thinking about network centrality is still mostly defined by the work of Freeman [56] and Bonacich [57].

While consumption and production have been separated for a long time in capitalist economy, due to social changes in many areas of production, especially those connected to media and internet, prosumption has gained a greater centrality in current capitalism (Blättel-Mink and Hellmann 2009; Ritzer and Jurgenson 2010).

The current study highlighted the centrality of the cared for but there is little research investigating their experiences.

We calculated degree (k), betweenness centrality (CBtw), current information flow (CCif), bridging centrality (CBdg), and clustering coefficient (CClu) for each applicable protein in the complete interaction network (Materials and Methods).

The first PC correlates strongly with degree, betweenness centrality, and current information flow, whereas the second PC correlates strongly and positively with bridging centrality and strongly and negatively with the clustering coefficient.