A Spectral Framework for Evaluating Geodesic Distances Between Graphs

• URL: http://arxiv.org/abs/2406.10500v3
• Date: Fri, 15 Aug 2025 05:34:43 GMT
• Title: A Spectral Framework for Evaluating Geodesic Distances Between Graphs
• Authors: Soumen Sikder Shuvo, Ali Aghdaei, Zhuo Feng,
• Abstract summary: We introduce a novel metric named Graph Geodesic Distance (GGD) to quantify the differentiation between graph data samples.<n>The proposed GGD metric can effectively quantify dissimilarities between two graphs by encapsulating their differences in key structural (spectral) properties.<n>We extend the application of GGD beyond graph classification to stability analysis of GNNs and the quantification of distances between datasets.
• Score: 4.110108749051657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a spectral framework for quantifying the differentiation between graph data samples by introducing a novel metric named Graph Geodesic Distance (GGD). For two different graphs with the same number of nodes, our framework leverages a spectral graph matching procedure to find node correspondence so that the geodesic distance between them can be subsequently computed by solving a generalized eigenvalue problem associated with their Laplacian matrices. For graphs of different sizes, a resistance-based spectral graph coarsening scheme is introduced to reduce the size of the larger graph while preserving the original spectral properties. We show that the proposed GGD metric can effectively quantify dissimilarities between two graphs by encapsulating their differences in key structural (spectral) properties, such as effective resistances between nodes, cuts, and the mixing time of random walks. Through extensive experiments comparing with state-of-the-art metrics, such as the latest Tree-Mover's Distance (TMD), the proposed GGD metric demonstrates significantly improved performance for graph classification, particularly when only partial node features are available. Furthermore, we extend the application of GGD beyond graph classification to stability analysis of GNNs and the quantification of distances between datasets, highlighting its versatility in broader machine learning contexts.

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