Michael Bronstein, University of Oxford, UK; ABSTRACT: The message-passing paradigm has been the “battle horse” of deep learning on graphs for several years, making graph neural networks a big success in a wide range of applications, from particle physics to protein design. From a theoretical viewpoint, it established the link to the Weisfeiler-Lehman hierarchy, allowing to analyse the expressive power of GNNs.
IEEE-Affiliated Group Name: CIS
URL: https://resourcecenter.cis.ieee.org/conferences/wcci-2022/ciswcci2022con0250
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