A target-aware solver-free data generation pipeline plus an LPGNN that uses linear-programming residuals produces fast, correctly labeled training data and improves GNN-based SAT prediction.
Graph data augmentation for graph machine learning: A survey.arXiv preprint arXiv:2202.08871
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AEGIS is an edge resampling framework that enhances link prediction in edge-sparse bipartite graphs, showing benefits from semantic augmentation on text-rich data.
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Target-Aware Data Augmentation for SAT Prediction
A target-aware solver-free data generation pipeline plus an LPGNN that uses linear-programming residuals produces fast, correctly labeled training data and improves GNN-based SAT prediction.
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AEGIS: Authentic Edge Growth In Sparsity for Link Prediction in Edge-Sparse Bipartite Knowledge Graphs
AEGIS is an edge resampling framework that enhances link prediction in edge-sparse bipartite graphs, showing benefits from semantic augmentation on text-rich data.