GRASP: Graph Alignment through Spectral Signatures
This addresses the graph alignment problem for applications like social network analysis and bioinformatics, offering a novel approach but is incremental in adapting existing concepts to a new domain.
The paper tackles the graph alignment problem by introducing GRASP, a method that uses functional maps and Laplacian eigenvectors to align nodes based on multiscale structural characteristics, and it outperforms state-of-the-art methods in experiments with high noise levels.
What is the best way to match the nodes of two graphs? This graph alignment problem generalizes graph isomorphism and arises in applications from social network analysis to bioinformatics. Some solutions assume that auxiliary information on known matches or node or edge attributes is available, or utilize arbitrary graph features. Such methods fare poorly in the pure form of the problem, in which only graph structures are given. Other proposals translate the problem to one of aligning node embeddings, yet, by doing so, provide only a single-scale view of the graph. In this paper, we transfer the shape-analysis concept of functional maps from the continuous to the discrete case, and treat the graph alignment problem as a special case of the problem of finding a mapping between functions on graphs. We present GRASP, a method that first establishes a correspondence between functions derived from Laplacian matrix eigenvectors, which capture multiscale structural characteristics, and then exploits this correspondence to align nodes. Our experimental study, featuring noise levels higher than anything used in previous studies, shows that GRASP outperforms state-of-the-art methods for graph alignment across noise levels and graph types.