Abstract
AbstractResearch leadership is of great significance to the research collaboration, especially in large scale projections. Characterizing the mechanisms and the processes of research leadership flow diffusion become essential to understand the knowledge flow diffusion in research collaboration. In this paper, we systemically analyze the differences in possibilities research leadership flow occurs between two researchers as seen from the effect of assortative mixing, preferential attachment, triadic closure, and reciprocity via Exponential Random Graph Model (ERGM). We demonstrate that combining both the researchers’ attributes and topological feature effects (assortative mixing, preferential attachment, triadic closure and reciprocity) can better characterize the diffusion of research leadership flow.KeywordsExponential Random Graph Model (ERGM)Research leadershipResearch collaborationSocial network analysis
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