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Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs

By Daniel Neil and others
In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowledge graphs (KGs). We introduce a regularized attention mechanism to GCNNs that not only improves performance on clean datasets, but also favorably accommodates noise... Show more
December 1, 2018
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