Multiscale Graph Comparison via the Embedded Laplacian Discrepancy

TL;DR

This paper introduces the Embedded Laplacian Discrepancy (ELD), a fast and effective method for multiscale graph comparison based on spectral embeddings and Wasserstein distances, addressing eigenvector ambiguity issues.

Contribution

The paper proposes ELD, a novel spectral graph comparison method that handles eigenvector ambiguities and is computationally efficient for graphs of different sizes.

Findings

ELD effectively compares graphs based on community structures.

ELD is invariant under graph isomorphism for simple spectra.

The perturbation approach stabilizes ELD for non-simple spectra.

Abstract

Laplacian eigenvectors capture natural community structures on graphs and are widely used in spectral clustering and manifold learning. The use of Laplacian eigenvectors as embeddings for the purpose of multiscale graph comparison has however been limited. Here we propose the Embedded Laplacian Discrepancy (ELD) as a simple and fast approach to compare graphs (of potentially different sizes) based on the similarity of the graphs' community structures. The ELD operates by representing graphs as point clouds in a common, low-dimensional space, on which a natural Wasserstein-based distance can be efficiently computed. A main challenge in comparing graphs through any eigenvector-based approaches is the potential ambiguity that could arise due to sign-flips and basis symmetries. The ELD leverages a simple symmetrization trick to bypass any sign ambiguities. For comparing graphs that do not have any ambiguities due to basis symmetries (i.e. the spectrums are simple), we show that the ELD becomes a natural pseudo-metric that enjoys nice properties such as invariance under graph isomorphism. For comparing graphs with non-simple spectrums, we propose a procedure to approximate the ELD via a simple perturbation technique to resolve any ambiguity from basis symmetries. We show that such perturbations are stable using matrix perturbation theory under mild assumptions that are straightforward to verify in practice. We demonstrate the excellent applicability of the ELD approach on both simulated and real datasets.