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This article introduces a number of methods that can be useful for examining the emergence of large-scale structures in collaboration networks. The study contributes to sociological research by investigating how clusters of research collaborators evolve and sometimes percolate in a collaboration network. Typically, we find that in our networks, one cluster among the leading ones eventually wins the growth race by percolating through the network, spanning it and rapidly filling up a significant volume of it. We show how this process is governed by the dynamics of cluster growth in the network. When operating in a percolating regime, this class of networks possesses many useful functional properties, which have important sociological implications. We first develop the methodological tools to perform a study of the intrinsic clustering process. Then, to understand the actual large-scale structure formation process in the network, we apply the theoretical methods to simulate a number of realistic scenarios, including one based on actual data on the collaboration behavior of a sample of researchers. From the perspective of social science research, our methods can be adapted to suit the application domains of many other types of real social processes. [ABSTRACT FROM AUTHOR]

• The co-authorship networks constructed from the bibliometric data of research published in the area of Artificial Intelligence and Machine Learning are investigated. • The dichotomous networks are highly assortative and the corresponding strength-coupled networks are highly cohesive. • The distribution profile, not for the degrees but for the weighted degrees, is smooth and well described by power-law with exponential cut-off. • Dominance of affiliations from domestic institutions of the authors with high closeness centrality is observed for highly productive cases. • This study suggests that the dominance of domestic affiliations for central authors influences and catalyses the collaborative research. Extant studies suggest that the proximity between the researchers and their structural positioning in the collaboration network may influence productivity and performance in collaboration research. In this paper, we analyze the co-authorship networks of the three countries, viz. the USA, China, and India, constructed in consecutive non-overlapping 5-year long time windows from bibliometric data of research papers published in the past decade in the rapidly evolving area of Artificial Intelligence and Machine Learning (AI&ML). Our analysis relies on the observations ensued from a comparison of the statistical properties of the evolving networks. We consider macro-level network properties which describe the global characteristics, such as degree distribution, assortativity, and large-scale cohesion etc., as well as micro-level properties associated with the actors who have assumed central positions, defining a core in the network assembly with respect to closeness centrality measure. For the analysis of the core actors, who are well connected with a large number of other actors, we consider share of their affiliations with domestic institutes. We find dominant representation of domestic affiliations of the core actors for high productivity cases, such as China in the second time window and the USA in the first and second both. Our study, therefore, suggests that the domestic affiliation of the core actors, who could access network resources more efficiently than other actors, influences and catalyzes the collaborative research. [ABSTRACT FROM AUTHOR]