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Science Genome
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About

Science Genome, which is a new quantitative framework designed to investigate Science of Science using representation learning and graph embedding, will take advantage of the availability of digitized bibliographic datasets and powerful computational methods, such as machine learning with deep neural networks, to tap into hidden information present in complex scholarly graphs. The project is funded by the Minerva Research Initiative Award for up to $4.4 million from the United States Department of Defense.

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Funding

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People

PI & Co-PI

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    Yong-Yeol Ahn
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    Staša Milojević
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    Alessandro Flammini
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    Filippo Menczer
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    Sriraam Natarajan

Research Scientists & Postdoctoral Researcher

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    Filipi Silva
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    Sadamori Kojaku
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    Attila Varga
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    Xiaoran Yan

Graduate Research Assistants

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    Dakota Murray
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    Lili Miao
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    Clara Boothby
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    Isabel Constantino
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    Devendra Singh Dhami
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    Siwen Yan
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    Saurabh Mathur
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    Rachith Aiyappa
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    Munjung Kim
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    Anne Kavalerchik
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    Govind Gandhi

Visitors

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    Damin Lee

Technical Support Team

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    Ben Serrett
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    Nick Liu

Publications

  • The latent structure of global scientific development
    Author(s): Miao L, Murray D, Jung WS, Larivière V, Sugimoto CR, Ahn YY.
    Nature Human Behaviour, 1-2 (2022)
  • Robustness modularity in complex networks
    Author(s): Filipi N. Silva, Aiiad Albeshri, Vijey Thayananthan, Wadee Alhalabi, and Santo Fortunato
    Phys. Rev. E 105, 054308 (2022)
  • The Narrowing of Literature Use and the Restricted Mobility of Papers in the Sciences.
    Author(s): Varga Attila
    Proceedings of the National Academy of Sciences of the United States of America 119(17): e2117488119 (2022)
  • Visualizing big science projects
    Author(s): Börner, K., Silva, F. N. and Milojević, S.
    Visualizing big science projects. Nature Reviews Physics. 3, 753–761 (2021)
  • Residual2Vec: Debiasing graph embedding with random graph
    Author(s): Sadamori Kojaku, Jisung Yoon, Isabel Constantino, and Yong-Yeol Ahn
    NeurIPS (2021)
  • Measuring the Unmeasurable in the Science of Science
    Author(s): Lingfei Wu, Aniket Kittur, Hyejin Youn, Staša Milojević, Erin Leahey, Stephen Fiore, Yong-Yeol Ahn
    Journal of Informetrics, 16-2, (2022)
  • Gender inequities in the online dissemination of scholars' work
    Author(s): Vasarhelyi, O., Zakhlebin, I., Milojević, S., & Horvat, A-E.
    PNAS, 9/20/2021
  • Non-parametric Learning of Embeddings for Relational Data Using Gaifman Locality Theorem
    Author(s): Dhami, Devendra Singh, et al.
    International Conference on Inductive Logic Programming. Springer, Cham, 2021
  • Unsupervised embedding of trajectories captures the latent structure of mobility
    Author(s): Dakota Murray*, Jisung Yoon*, Sadamori Kojaku, Rodrigo Costas, Woo-Sung Jung, Staša Milojević, Yong-Yeol Ahn
    (Under review)
  • On the Stability of Citation Networks
    Author(s): A. Benatti, H. F. de Arruda, C. H. Comin, F. N. Silva, L. da F. Costa
    Under review in Journal of Informetrics (2021)
  • CADRE: A Cloud-Based Data Service for Big Bibliographic Data
    Author(s): Xiaoran Yan, Guangchen Ruan, Dimitar Nikolov, Matthew Hutchinson, Chathuri Peli Kankanamalage, Ben Serrette, James McCombs, Alan Walsh, Esen Tuna, Valentin Pentchev
    CIKM 2021
  • Stability through Constant Attrition: Turnover, Growth and the Career Age of the Scientific Workforce
    Author(s): Boothby, C., Milojević, S., Lariviere, V., Radicchi, F., & Sugimoto, C. R.
    (In Prep.)
  • Explainable Models via Compression of Tree Ensembles
    Author(s): Yan, Siwen & Natarajan, Sriraam & Joshi, Saket & Khardon, Roni & Tadepalli, Prasad.
    Machine Learning Journal, 2022 (To appear)