FC-GSSL improves graph SSL by generating high-frequency biased corrupted graphs via low-frequency contribution-based corruption, reconstructing low-frequency features in an autoencoder, and aligning multi-view representations to fuse frequency bands.
Specformer: Spectral graph neural networks meet transformers.arXiv preprint arXiv:2303.01028
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
S³GNN mitigates oversquashing in message-passing networks via lightweight global mixing without strong prior assumptions, yielding up to 10x error reduction and 50% fewer parameters across multiple domains.
Contrastive FUSE learns node embeddings from partial pairwise supervision and structural signals alone by optimizing a spectral contrastive objective with a lightweight modularity approximation, yielding competitive performance and runtime gains on citation and co-purchase graphs.
citing papers explorer
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Graph self-supervised learning based on frequency corruption
FC-GSSL improves graph SSL by generating high-frequency biased corrupted graphs via low-frequency contribution-based corruption, reconstructing low-frequency features in an autoencoder, and aligning multi-view representations to fuse frequency bands.
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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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S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
S³GNN mitigates oversquashing in message-passing networks via lightweight global mixing without strong prior assumptions, yielding up to 10x error reduction and 50% fewer parameters across multiple domains.
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Fast and Featureless Node Representation Learning with Partial Pairwise Supervision
Contrastive FUSE learns node embeddings from partial pairwise supervision and structural signals alone by optimizing a spectral contrastive objective with a lightweight modularity approximation, yielding competitive performance and runtime gains on citation and co-purchase graphs.