Mass-conserving diffusion-based dynamics on graphs
Jeremy Budd, Yves van Gennip

TL;DR
This paper extends the graph-based phase separation technique to include mass conservation, providing theoretical analysis and establishing a link between mass-conserving Allen-Cahn flows and MBO schemes, with initial steps towards multi-class extensions.
Contribution
It introduces a mass-conserving Allen-Cahn equation on graphs and derives a corresponding MBO scheme, extending previous work to the mass-conserving setting with theoretical guarantees.
Findings
Proved existence and uniqueness of the mass-conserving flow.
Established convergence of the semi-discrete scheme.
Connected mass-conserving Allen-Cahn flow to MBO scheme.
Abstract
An emerging technique in image segmentation, semi-supervised learning, and general classification problems concerns the use of phase-separating flows defined on finite graphs. This technique was pioneered in Bertozzi and Flenner (2012), which used the Allen-Cahn flow on a graph, and was then extended in Merkurjev, Kostic and Bertozzi (2013) using instead the Merriman-Bence-Osher (MBO) scheme on a graph. In previous work by the authors, Budd and Van Gennip (2019), we gave a theoretical justification for this use of the MBO scheme in place of Allen-Cahn flow, showing that the MBO scheme is a special case of a "semi-discrete" numerical scheme for Allen-Cahn flow. In this paper, we extend this earlier work, showing that this link via the semi-discrete scheme is robust to passing to the mass-conserving case. Inspired by Rubinstein and Sternberg (1992), we define a mass-conserving…
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