Abstract: Graph data modeling is nontrivial due to the challenges to ensure model interpretability and handle data uncertainty. While methods derived from deep learning models, such as graph neural ...
Abstract: Graph Neural Networks (GNNs) have emerged as promising tools in graph semi-supervised learning. They acquire low-dimensional node embeddings for downstream tasks by aggregating and updating ...
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