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Learning on knowledge graph dynamics provides an early warning of impactful research

Nature Biotechnology volume 39, pages 1300–1307 (2021)Cite this article

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Abstract

The scientific ecosystem relies on citation-based metrics that provide only imperfect, inconsistent and easily manipulated measures of research quality. Here we describe DELPHI (Dynamic Early-warning by Learning to Predict High Impact), a framework that provides an early-warning signal for ‘impactful’ research by autonomously learning high-dimensional relationships among features calculated across time from the scientific literature. We prototype this framework and deduce its performance and scaling properties on time-structured publication graphs from 1980 to 2019 drawn from 42 biotechnology-related journals, including over 7.8 million individual nodes, 201 million relationships and 3.8 billion calculated metrics. We demonstrate the framework’s performance by correctly identifying 19/20 seminal biotechnologies from 1980 to 2014 via a blinded retrospective study and provide 50 research papers from 2018 that DELPHI predicts will be in the top 5% of time-rescaled node centrality in the future. We propose DELPHI as a tool to aid in the construction of diversified, impact-optimized funding portfolios.

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Fig. 1: Collecting, structuring, computing on and learning an early-warning signal of scientific impact from dynamic knowledge graphs.
Fig. 2: Dynamics of knowledge graph structure contain information about future scientific impact.
Fig. 3: DELPHI leverages temporal dynamics to identify high-impact research early and with state-of-the-art performance characteristics when focused on biotechnology publications.
Fig. 4: DELPHI correctly identifies historical biotechnology breakthroughs in a blinded back-testing.
Fig. 5: In a world of expanding science and limited resources, quantitative approaches such as DELPHI can be used to help guide research funding allocations to maximize scientific return on investment.

Data availability

The data analyzed are available for download from https://www.lens.org/. Exemplary datasets and retrieval code are further available from GitHub as described in the ‘Code availability’ section.

Code availability

Exemplary code, datasets, trained models, a visualization application to aid in the analysis of results and Docker-based installation instructions are all available from GitHub at https://github.com/jameswweis/delphi.

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Acknowledgements

This work was supported by the consortia of sponsors of the MIT Media Lab and the MIT Center for Bits and Atoms. We thank the AWS Cloud Credits for Research program for computational infrastructure and the Lens Lab for providing publication data.

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Authors and Affiliations

  1. MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    James W. Weis & Joseph M. Jacobson

  2. Department of Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    James W. Weis

  3. MIT Center for Bits and Atoms, Massachusetts Institute of Technology, Cambridge, MA, USA

    Joseph M. Jacobson

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Contributions

J.W.W. and J.M.J. conceived the study. J.W.W. performed the data structuring, algorithm design and computational implementation. J.W.W. and J.M.J. drafted the manuscript and figures. J.M.J. supported and supervised the project.

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Correspondence to James W. Weis.

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The authors declare no competing interests.

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Peer review information Nature Biotechnology thanks Lutz Bornmann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The DELPHI framework exhibits strong performance characteristics across a range of definitions of high-impact and and model evaluation criteria.

(a) The DELPHI framework is based on a user-defined definition of high-impact, and the utility of the framework is robust to the specific parameters of that definition. DELPHI models were constructed with a range of threshold definitions between 5% and 25%, and evaluated across a range of criteria to demonstrate this robustness. (b) Those papers in the top 5% of our impact metric, time-rescaled node centrality, contain over 35% of total aggregate impact. As such, the high-impact threshold of 5% was chosen for this study.

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Supplementary Information

Supplementary Tables 1 and 2.

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Weis, J.W., Jacobson, J.M. Learning on knowledge graph dynamics provides an early warning of impactful research. Nat Biotechnol 39, 1300–1307 (2021). https://doi.org/10.1038/s41587-021-00907-6

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