Sparse Network Inference

Sparse Network Inference is a computational methodology within network inference that specifically aims to reconstruct network structures under the assumption of sparsity, typically by identifying a minimal set of significant interactions among a large number of potential connections.

132

Publications

11.7K

Citations

413

Authors

203

Institutions

Publications per year

2017–2026

76

Authors

413

  1. SS
    Santiago Segarra

    Rice University, United States

    3 publications · 72 citations

    3

    h-index

  2. JP
    Jean Pouget-Abadie

    Harvard University Press, United States

    3 publications · 58 citations

    3

    h-index

  3. AM
    Andrea Montanari

    Stanford University, United States

    3 publications · 115 citations

    3

    h-index

  4. AG
    Antonio G. Marqués

    Universidad Rey Juan Carlos, Spain

    3 publications · 55 citations

    3

    h-index

  5. TH
    Thibaut Horel

    Harvard University Press, United States

    3 publications · 58 citations

    3

    h-index

1–5 of 413

Venues

  1. arXiv (Cornell University)

    Repository · 25 publications · 2.2K citations

    17

    h-index

  2. Physical Review E

    PRE · 5 publications · 268 citations

    5

    h-index

  3. PLoS Computational Biology

    4 publications · 194 citations

    4

    h-index

  4. Lecture notes in computer science

    LNCS · Book series · 3 publications · 75 citations

    3

    h-index

  5. Neural Information Processing Systems

    Conference · 3 publications · 71 citations

    3

    h-index

1–5