Structural Diversity Drives Disruptive Scientific Innovation
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918498254454784 |
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| author | Peng, Yichun He, Saike Zhang, Peijie Zhao, Kang Yang, Yi Zhang, Ning Zhang, Qingpeng Zeng, Daniel Dajun Peng, Hao |
| author_facet | Peng, Yichun He, Saike Zhang, Peijie Zhao, Kang Yang, Yi Zhang, Ning Zhang, Qingpeng Zeng, Daniel Dajun Peng, Hao |
| contents | Scientific innovation increasingly depends on collaboration, yet the organizational structure that fosters breakthrough ideas remains poorly understood. Existing metrics - such as team size or compositional diversity - capture readily observable characteristics but not the deeper architecture of collaboration. We introduce Structural Diversity (SD): the extent to which a team bridges multiple distinct knowledge communities within its prior collaboration network. Using a century-scale dataset of 260 million scientific publications (1900-2025) and combining causal inference with a quasi-natural experiment based on a U.S. National Science Foundation policy change in 2012, we show that SD is a powerful and robust predictor of disruptive innovation, outperforming traditional team novelty indicators such as team freshness and edge density. Moreover, SD positively interacts with team size and is able to mitigate the well-known "curse of scale" by transforming scale from a liability into a resource for creative synthesis. We find that one mechanism underlying this effect is Disciplinary Integration (DI): teams with higher SD can more effectively combine heterogeneous knowledge into novel configurations. Our findings position SD as both a new theoretical construct and an actionable design principle for organizing scientific collaboration. By linking the architecture of team assembly to the dynamics of creative discovery, our work offers a structural explanation for how collective intelligence can be systematically engineered to foster disruptive innovation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12514 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Structural Diversity Drives Disruptive Scientific Innovation Peng, Yichun He, Saike Zhang, Peijie Zhao, Kang Yang, Yi Zhang, Ning Zhang, Qingpeng Zeng, Daniel Dajun Peng, Hao Social and Information Networks Computer Vision and Pattern Recognition Computers and Society Digital Libraries Applications Scientific innovation increasingly depends on collaboration, yet the organizational structure that fosters breakthrough ideas remains poorly understood. Existing metrics - such as team size or compositional diversity - capture readily observable characteristics but not the deeper architecture of collaboration. We introduce Structural Diversity (SD): the extent to which a team bridges multiple distinct knowledge communities within its prior collaboration network. Using a century-scale dataset of 260 million scientific publications (1900-2025) and combining causal inference with a quasi-natural experiment based on a U.S. National Science Foundation policy change in 2012, we show that SD is a powerful and robust predictor of disruptive innovation, outperforming traditional team novelty indicators such as team freshness and edge density. Moreover, SD positively interacts with team size and is able to mitigate the well-known "curse of scale" by transforming scale from a liability into a resource for creative synthesis. We find that one mechanism underlying this effect is Disciplinary Integration (DI): teams with higher SD can more effectively combine heterogeneous knowledge into novel configurations. Our findings position SD as both a new theoretical construct and an actionable design principle for organizing scientific collaboration. By linking the architecture of team assembly to the dynamics of creative discovery, our work offers a structural explanation for how collective intelligence can be systematically engineered to foster disruptive innovation. |
| title | Structural Diversity Drives Disruptive Scientific Innovation |
| topic | Social and Information Networks Computer Vision and Pattern Recognition Computers and Society Digital Libraries Applications |
| url | https://arxiv.org/abs/2605.12514 |