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Showing 1–8 of 8 results
Advanced filters: Author: Tina Eliassi-Rad Clear advanced filters

  • Assigning entities to teams for task completion under constraints is a complex combinatorial optimization challenge. Here, the authors introduce a novel algorithm using hypergraph-based search to optimize resilience and diffusion, demonstrating superior robustness to node removal attacks compared to traditional methods, with implications for diverse fields requiring resilient team configurations

    • Guilherme Ferraz de Arruda
    • Wan He
    • Tina Eliassi-Rad
    ResearchOpen Access
    Communications Physics
    Volume: 9, P: 1-15

  • Bolstering the broad and deep applicability of graph neural networks, Heydaribeni et al. introduce HypOp, a framework that uses hypergraph neural networks to solve general constrained combinatorial optimization problems. The presented method scales and generalizes well, improves accuracy and outperforms existing solvers on various benchmarking examples.

    • Nasimeh Heydaribeni
    • Xinrui Zhan
    • Farinaz Koushanfar
    Research
    Nature Machine Intelligence
    Volume: 6, P: 664-672

  • Using registry data from Denmark, Lehmann et al. create individual-level trajectories of events related to health, education, occupation, income and address, and also apply transformer models to build rich embeddings of life-events and to predict outcomes ranging from time of death to personality.

    • Germans Savcisens
    • Tina Eliassi-Rad
    • Sune Lehmann
    Research
    Nature Computational Science
    Volume: 4, P: 43-56

  • A study shows that, although the number of incarcerated people in the USA decreased during the first year of the COVID-19 pandemic, the fraction of incarcerated Black and Latino individuals increased.

    • Brennan Klein
    • C. Brandon Ogbunugafor
    • Elizabeth Hinton
    ResearchOpen Access
    Nature
    Volume: 617, P: 344-350

  • State-of-the-art machine learning models in drug discovery fail to reliably predict the binding properties of poorly annotated proteins and small molecules. Here, the authors present AI-Bind, a machine learning pipeline to improve generalizability and interpretability of binding predictions.

    • Ayan Chatterjee
    • Robin Walters
    • Giulia Menichetti
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-15

  • The structure of ties in social networks determines who receives information first, and those early opportunities often confer a competitive advantage. The authors develop the H3 hypergraph model, which reveals how hyperedge homophily drives inequality in information access and suggests that targeted interventions informed by higher-order dynamics can help close those gaps.

    • Moritz Laber
    • Samantha Dies
    • Tina Eliassi-Rad
    ResearchOpen Access
    Communications Physics
    Volume: 9, P: 1-16

  • This Perspective discusses the challenges for social science practices imposed by the ubiquity of algorithms and large-scale measurement and what should—and should not—be measured in societies pervaded by algorithms.

    • Claudia Wagner
    • Markus Strohmaier
    • Tina Eliassi-Rad
    Reviews
    Nature
    Volume: 595, P: 197-204