Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.
StATIK: Structure and Text for Inductive Knowledge Graph Com- pletion
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BioBLP is a modular embedding framework for multimodal biomedical KGs supporting heterogeneous attributes and missing data, with a pretraining strategy that improves results on drug-protein interaction prediction especially for low-degree entities.
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Explaining Graph Neural Networks for Node Similarity on Graphs
Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.
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BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs
BioBLP is a modular embedding framework for multimodal biomedical KGs supporting heterogeneous attributes and missing data, with a pretraining strategy that improves results on drug-protein interaction prediction especially for low-degree entities.