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.
The output of the layer isS, for which we have ∥S−S ′∥2 = vuut NX i=1 RX j=1 |sij −s ′ ij|2 ≤4 r θ n vuut NX i=1 RX j=1 ∥Hi −H ′ i∥2 F = 4 r θR n ∥H−H ′∥F This finished the proof
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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.