GRASP: Scalable Graph Alignment by Spectral Corresponding Functions
Authors: 
Judith Hermanns, Konstantinos Skitsas, Anton Tsitsulin, Marina Munkhoeva, Alexander Kyster, Simon Nielsen, Alexander M. Bronstein, Davide Mottin, Panagiotis KarrasAuthors Info & Claims
Judith Hermanns, Konstantinos Skitsas, Anton Tsitsulin, Marina Munkhoeva, Alexander Kyster, Simon Nielsen, Alexander M. Bronstein, Davide Mottin, Panagiotis KarrasAuthors Info & ClaimsArticle No.: 50, Pages 1 - 26
Abstract
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 article, 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. We enhance the basic form of GRASP by altering two of its components, namely the embedding method and the assignment procedure it employs, leveraging its modular, hence adaptable design. Our experimental study, featuring noise levels higher than anything used in previous studies, shows that the enhanced form of GRASP outperforms scalable state-of-the-art methods for graph alignment across noise levels and graph types, and performs competitively with respect to the best non-scalable ones. We include in our study another modular graph alignment algorithm, CONE, which is also adaptable thanks to its modular nature, and show it can manage graphs with skewed power-law degree distributions.
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Index Terms
- GRASP: Scalable Graph Alignment by Spectral Corresponding Functions
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May 2023
364 pages
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Publication History
Published: 24 February 2023
Online AM: 15 September 2022
Accepted: 19 August 2022
Revised: 18 August 2022
Received: 05 December 2021
Published in TKDD Volume 17, Issue 4
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