Table 2.

Performance comparison on USPTO-50 K dataset across different training approaches

Model	Initial accuracy (%) ± std	Symmetry-aware accuracy (%) ± std
SAMMNet
GIN	88.51 ± 0.07	97.37 ±0.06
GCN	87.18 ± 0.12	95.66 ± 0.08
GraphSAGE	88.20 ± 0.11	97.02 ± 0.05
Vanilla
GIN	87.64 ± 0.09	96.46 ± 0.06
GCN	86.34 ± 0.11	94.89 ± 0.08
GraphSAGE	86.21 ±0.1	95.32 ±0.07
Transfer learning
GIN	86.65 ± 0.1	95.5 ± 0.04
GCN	85.15 ± 0.12	93.73 ± 0.08
GraphSAGE	84.78 ± 0.14	93.87 ± 0.05

Bold values indicate the best-performing results for each evaluation metric