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Creators/Authors contains: "Frongillo, Rafael"

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  1. ; ; ( , SIAM Review)
    The metric dimension of a graph is the smallest number of nodes required to identify allother nodes uniquely based on shortest path distances. Applications of metric dimensioninclude discovering the source of a spread in a network, canonically labeling graphs, andembedding symbolic data in low-dimensional Euclidean spaces. This survey gives a self-contained introduction to metric dimension and an overview of the quintessential resultsand applications. We discuss methods for approximating the metric dimension of generalgraphs, and specific bounds and asymptotic behavior for deterministic and random familiesof graphs. We conclude with related concepts and directions for future work. 
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    Free, publicly-accessible full text available November 1, 2024
  2. ; ; ; ; ( , Discrete Applied Mathematics)
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  3. ; ; ; ( , Proceedings of Machine Learning Research)
    Accepted Manuscript Full Text Available
  4. ; ( , Games and Economic Behavior)
    Full Text Available 
  5. ; ; ; ( , Economics and Computation)
    Full Text Available 
  6. ; ( , Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  7. ; ; ( , Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  8. ; ( , Annals of the Institute of Statistical Mathematics)
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  9. ; ; ( , Proceedings of Machine Learning Research)
    Conference on Learning Theory 2020 
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    Accepted Manuscript Full Text Available
  10. ; ( , Journal of machine learning research)
    Accepted Manuscript Full Text Available