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Fused Gromov-Wasserstein Alignment for Graph Edit Distance Computation and Beyond

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FGWAlign achieves a significant reduction of computation errors by over 80% across various graph datasets.
Demonstrating 15–60× speedup, FGWAlign effectively balances efficiency and accuracy in GED computation.
The method utilizes Fused Gromov-Wasserstein distance to optimize graph edit distance measurements.
FGWAlign's framework accommodates multi-relational graphs, expanding its versatility for diverse applications.

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This method reduces computation errors by over 80% in graph alignment, indicating promising potential for graph analysis.

Tang et al. (2025) studied this question.

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