The generation mechanism of research leadership in international collaboration based on GERGM: a case from the field of artificial intelligence

Conducting an in-depth analysis of 235,746 research papers in the field of artificial intelligence spanning from 2001 to 2020, this study quantified the extent of research leadership in international collaborations by discerning the country of the corresponding author. To comprehensively investigate both endogenous and exogenous effects, we employed the Generalized Exponential Random Graph Model, an advanced methodology adept at characterizing network structures with real-valued edges. This research elucidates the pivotal role of intrinsic structural factors influenced by edge dependencies and evaluates their impact on research leadership in international collaborations. Specifically, our findings reveal a positive and significant effect of the mutual effect and the transitivity effect. Furthermore, language and geography no longer play a significant role in generating international research collaborations between two countries. Additionally, scientific productivity also holds an important position in generating research leadership. However, R&D expenditures no longer facilitate the establishment of leadership for international research collaboration.