GRASP: Graph Alignment through Spectral Signatures
Judith Hermanns, Anton Tsitsulin, Marina Munkhoeva, Alex Bronstein, Davide Mottin, Panagiotis Karras
APWeb-WAIM 2021

With a few removed edges, REGAL, an alignment method based on localfeatures, fails to correctly align the distorted Karate club graph to the original; GRASPidentifies most of nodes (correctly aligned nodes in green).
TL;DR
We propose GRASP, short for GRaph Alignment through SPectral Signatures, a principled approach towards detecting a good alignment among graphs, grounded on their spectral characteristics, i.e., eigenvalues and eigenvectors of their Laplacian matrices.
In this paper:
- We propose GRASP, a graph alignment method based on spectral graph characteristics
- We show GRASP effectiveness in recovering real-graph align-ments
- GRASP delivers higher accuracy as the state of the art in comparable time
- We release the code for further comparisons
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