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    Graph learning· 2017

    Graph Attention Networks

    Petar Veličković (first author) · 2017 · published 2018

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    DOI 10.17863/cam.48429arXiv 1710.10903OpenAlex W2766453196

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    Why it matters

    Made attention-weighted neighbor aggregation a standard graph-learning mechanism.

    Condensed from the expert lists' notes.

    The record

    First released
    2017 (from arXiv 1710.10903)
    Published
    2018
    Citations
    28,961 citations, as counted by Papers With Code (CC BY-SA 4.0)
    Named by
    both expert lists
    Score
    0.680 · computed rank 83 · consensus 0.96, must-read 0.5, citations percentile 0.978 (2010s · graphs), awards 0, existing lists 0. Method

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.