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Found 3 papers, 1 papers with code
Design Requirements for Human-Centered Graph Neural Network Explanations
Graph neural networks (GNNs) are powerful graph-based machine-learning models that are popular in various domains, e. g., social media, transportation, and drug discovery.
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition
Many Graph Neural Networks (GNNs) are proposed for Knowledge Graph Embedding (KGE).
Self-attention Presents Low-dimensional Knowledge Graph Embeddings for Link Prediction
Notably, we yield our promising results with a significant reduction of 66. 9% in the dimensionality of embeddings compared to the five best recent state-of-the-art competitors on average.
Ranked #16 on
Link Prediction
on FB15k-237
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