Graph ML Research at Twitter with Michael Bronstein - #394
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Enhancing Graph Neural Networks through Substructural Insights
This chapter examines the complexities of training graph neural networks in temporal contexts and their expressivity, while addressing limitations in detecting critical graph structures. It introduces a novel approach that integrates local descriptors into message passing architectures to improve performance and expressiveness in representing graph data.
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