GraphVec produces transferable fixed-dimensional graph embeddings via spectral features from multi-scale global graphs and a convergent mean-alignment procedure, outperforming baselines on cross-domain few-shot classification and clustering across 13 datasets.
Graphany: A foundation model for node classification on any graph.arXiv preprint arXiv:2405.20445, 2024a
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ALL-IN projects node features to a random shared space and uses covariance operators to produce representations invariant to input feature permutations and orthogonal transformations, enabling transfer across graph datasets.
SPG is a graph foundation model using spectral decomposition via Chebyshev filters and Gromov-Wasserstein prototypes for improved cross-graph transferability.
citing papers explorer
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GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning
GraphVec produces transferable fixed-dimensional graph embeddings via spectral features from multi-scale global graphs and a convergent mean-alignment procedure, outperforming baselines on cross-domain few-shot classification and clustering across 13 datasets.
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Bridging Input Feature Spaces Towards Graph Foundation Models
ALL-IN projects node features to a random shared space and uses covariance operators to produce representations invariant to input feature permutations and orthogonal transformations, enabling transfer across graph datasets.
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A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation
SPG is a graph foundation model using spectral decomposition via Chebyshev filters and Gromov-Wasserstein prototypes for improved cross-graph transferability.