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.
Regarding data splitting, we randomly choose 50 graphs in each class for training, and the remaining samples are used for testing
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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.