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This paper introduces ReTA, a reinforcement learning-based framework for dynamically augmenting electronic health record graphs with external knowledge graphs to improve prediction tasks like diagnosis and mortality.
This paper adapts instance discrimination self-supervised learning to link prediction in graphs, proposing new models L-GRACE and L-BGRL that operate on link representations and improve performance especially on unattributed graphs.