SPECTRA improves molecular property regression on underrepresented targets via spectral graph generation with rarity-aware budgeting and Laplacian interpolation, paired with edge-aware Chebyshev GNNs, yielding competitive benchmark performance at lower compute cost.
Smote: synthetic minority over-sampling technique
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3years
2025 3verdicts
UNVERDICTED 3representative citing papers
AEGIS is an edge resampling framework that enhances link prediction in edge-sparse bipartite graphs, showing benefits from semantic augmentation on text-rich data.
Proposes three metrics for inter-column logical relationships in synthetic tabular data and reports that current generators often fail to preserve them on an industrial dataset.
citing papers explorer
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SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression
SPECTRA improves molecular property regression on underrepresented targets via spectral graph generation with rarity-aware budgeting and Laplacian interpolation, paired with edge-aware Chebyshev GNNs, yielding competitive benchmark performance at lower compute cost.
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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.
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Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data Generation
Proposes three metrics for inter-column logical relationships in synthetic tabular data and reports that current generators often fail to preserve them on an industrial dataset.