Combinatorial and Hodge Laplacians: Similarity and Difference
Emily Ribando-Gros, Rui Wang, Jiahui Chen, Yiying Tong, Guo-Wei Wei

TL;DR
This paper compares the combinatorial and Hodge Laplacians, introduces Boundary-Induced Graph Laplacians using Discrete Exterior Calculus, and experimentally analyzes their convergence for shape analysis.
Contribution
It introduces BIG Laplacians for boundary-aware discretization and provides a detailed comparison with existing Laplacians, bridging gaps in spectral geometry applications.
Findings
BIG Laplacians characterize data topology and shape.
Eigenvalues of BIG Laplacians converge to Hodge Laplacian eigenvalues for simple shapes.
The paper clarifies the applicability differences between combinatorial and Hodge Laplacians.
Abstract
As key subjects in spectral geometry and combinatorial graph theory respectively, the (continuous) Hodge Laplacian and the combinatorial Laplacian share similarities in revealing the topological dimension and geometric shape of data and in their realization of diffusion and minimization of harmonic measures. It is believed that they also both associate with vector calculus, through the gradient, curl, and divergence, as argued in the popular usage of "Hodge Laplacians on graphs" in the literature. Nevertheless, these Laplacians are intrinsically different in their domains of definitions and applicability to specific data formats, hindering any in-depth comparison of the two approaches. To facilitate the comparison and bridge the gap between the combinatorial Laplacian and Hodge Laplacian for the discretization of continuous manifolds with boundary, we further introduce…
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Taxonomy
TopicsTopological and Geometric Data Analysis · Digital Image Processing Techniques · Computational Geometry and Mesh Generation
