In graph neural networks (GNNs), vertex representation learning is a crucial task that involves learning compact and meaningful representations of nodes in a graph. These representations can be used for various downstream tasks, such as node classification, link prediction, and graph classification.
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Vertex BD automates repetitive tasks to streamline the design-to-manufacturing workflow: