Neural embeddings of collaboration networks predict citation impact and innovation

• We propose a method based on neural embedding methods to quantify structural diversity for individual teams. • The structural diversity effectively differentiates scientists across various scientific domains. • The structural diversity predicts citation impact, novelty, and disruption of the produced papers. • Structural diversity plays a preeminent role relative to other diversity measures. Scientific collaboration is a key driver of breakthroughs in contemporary science. While prior research has primarily focused on team diversity based on individual attributes, quantifying structural diversity based on collaborative relationships at the individual paper level remains underexplored. In this study, we apply neural embedding techniques to quantify structural diversity at the paper level and examine its association with research outcomes. Leveraging the node2vec and GraphSAGE algorithms, we embed each scientist into a dense vector space using the Microsoft Academic Graph dataset, covering 4,772,781 papers written by 5,162,397 scientists from 1995 to 2016. We show that our embedding model effectively classifies scientists across scientific domains and accurately predicts their primary scientific domains. Crucially, our analysis reveals that structural diversity is strongly associated with citation impact, novelty, and disruption at the individual paper level. Specifically, a 1SD increase in structural diversity is associated with a 7.6%, 24.7%, and 2.9% increase in citation impact, and the odds of publishing novel and disruptive papers, respectively. These findings are generalizable across multiple model specifications, time spans, different team sizes, and scientific domains. Finally, our analysis reveals that structural diversity exhibits the strongest correlation with citation impact, novelty, and disruption among all examined diversity measures. Our study highlights the importance of fostering structurally diverse collaborations and has policy implications for institutions, funders, and governments aiming to support impactful and innovative research.