  Gender disparity in science is one of the most focused debating points among
authorities and the scientific community. Over the last few decades, numerous
initiatives have endeavored to accelerate gender equity in academia and
research society. However, despite the ongoing efforts, gaps persist across the
world, and more measures need to be taken. Using social network analysis,
natural language processing, and machine learning, in this study, we
comprehensively analyzed gender-specific patterns in the highly
interdisciplinary and evolving field of artificial intelligence for the period
of 2000-2019. Our findings suggest an overall increasing rate of mixed-gender
collaborations. From the observed gender-specific collaborative patterns, the
existence of disciplinary homophily at both dyadic and team levels is
confirmed. However, a higher preference was observed for female researchers to
form homophilous collaborative links. Our core-periphery analysis indicated a
significant positive association between having diverse collaboration and
scientific performance and experience. We found evidence in support of
expecting the rise of new female superstar researchers in the artificial
intelligence field.
