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Creators/Authors contains: "Kutyniok, Gitta"

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  1. ; ; (, Applied and Computational Harmonic Analysis)
    Full Text Available 
  2. ; ; (, Advances in neural information processing systems)
    This work provides the first theoretical study on the ability of graph Message Passing Neural Networks (gMPNNs) -- such as Graph Neural Networks (GNNs) -- to perform inductive out-of-distribution (OOD) link prediction tasks, where deployment (test) graph sizes are larger than training graphs. We first prove non-asymptotic bounds showing that link predictors based on permutation-equivariant (structural) node embeddings obtained by gMPNNs can converge to a random guess as test graphs get larger. We then propose a theoretically-sound gMPNN that outputs structural pairwise (2-node) embeddings and prove non-asymptotic bounds showing that, as test graphs grow, these embeddings converge to embeddings of a continuous function that retains its ability to predict links OOD. Empirical results on random graphs show agreement with our theoretical results. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; ; (, European Conference on Computer Vision)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, Journal of Mathematical Imaging and Vision)
    null (Ed.)
    Full Text Available