Call the shots or follow suit? Research on the evolution of artificial intelligence scientific collaboration networks from a leadership-participation perspective

Scientific research cooperation has become a key driver of technological innovation, knowledge dissemination, and social progress, enhancing the integration of different disciplines and improving the quality and quantity of research outcomes globally. Artificial intelligence (AI), characterized by globalization, interdisciplinarity, and deep industry-academia-research integration, advances technological progress. However, traditional models overlook the leadership-participation dynamic in research collaboration. This paper uses the Exponential Random Graph Model (ERGM) to analyze the attributes and structures of network nodes, revealing that reciprocity, transfer, preferential attachment, and homophily mechanisms are crucial for the formation and evolution of research collaboration networks, providing insights for policymakers and research teams.