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Mass-conserving diffusion-based dynamics on graphs

Published online by Cambridge University Press:  14 April 2021

J.M BUDD
Affiliation:
Delft Institute of Applied Mathematics (DIAM), Technische Universiteit Delft, Delft, The Netherlands e-mails: j.m.budd-1@tudelft.nl; y.vangennip@tudelft.nl
Y. VAN GENNIP
Affiliation:
Delft Institute of Applied Mathematics (DIAM), Technische Universiteit Delft, Delft, The Netherlands e-mails: j.m.budd-1@tudelft.nl; y.vangennip@tudelft.nl
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Abstract

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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, Multiscale Modeling and Simulation10(3), 1090–1118), which used the Allen–Cahn flow on a graph, and was then extended in Merkurjev et al. (2013, SIAM J. Imaging Sci.6(4), 1903–1930) using instead the Merriman–Bence–Osher (MBO) scheme on a graph. In previous work by the authors, Budd and Van Gennip (2020, SIAM J. Math. Anal.52(5), 4101–4139), 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, IMA J. Appl. Math.48, 249–264), we define a mass-conserving Allen–Cahn equation on a graph. Then, with the help of the tools of convex optimisation, we show that our earlier machinery can be applied to derive the mass-conserving MBO scheme on a graph as a special case of a semi-discrete scheme for mass-conserving Allen–Cahn. We give a theoretical analysis of this flow and scheme, proving various desired properties like existence and uniqueness of the flow and convergence of the scheme, and also show that the semi-discrete scheme yields a choice function for solutions to the mass-conserving MBO scheme.

Type
Papers
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

References

Acikmese, B. (2015) Spectrum of laplacians for graphs with self-loops. arXiv preprint arXiv:1505.08133.Google Scholar
Ambrosio, L., Gigli, L. & Savaré, G. (2008) Gradient Flows in Metric Spaces and in the Space of Probability Measures, 2nd ed. Lectures in Mathematics ETH Zürich, Birkhäuser Verlag, Basel.Google Scholar
Bae, E. & Merkurjev, E. (2017) Convex variational methods on graphs for multiclass segmentation of high-dimensional data and point clouds. J. Math. Imaging Vis. 58, 468493.CrossRefGoogle Scholar
Banach, S. & Saks, S. (1930) Sur la convergence dans les champs Lp. Studia Mathematica 2, 5157.CrossRefGoogle Scholar
Bence, J., Merriman, B. & Osher, S. (1992) Diffusion Generated Motion by Mean Curvature. CAM Report, 92-18, Department of Mathematics, University of California, Los Angeles.Google Scholar
Bertozzi, A. L. & Flenner, A. (2012) Diffuse interface models on graphs for analysis of high dimensional data. Multiscale Model. Simul. 10(3), 10901118.CrossRefGoogle Scholar
Blowey, J. F. & Elliott, C. M. (1991) The Cahn-Hilliard gradient theory for phase separation with non-smooth free energy, Part I: mathematical analysis. Eur. J. Appl. Math. 3, 233279.CrossRefGoogle Scholar
Blowey, J. F. & Elliott, C. M. (1992) The Cahn-Hilliard gradient theory for phase separation with non-smooth free energy, Part II: numerical analysis. Eur. J. Appl. Math 3, 147179.CrossRefGoogle Scholar
Blowey, J. F. & Elliott, C. M. (1993) Curvature dependent phase boundary motion and parabolic double obstacle problems. In: Ni, W. M., Peletier, L. A. and Vazquez, J. L. (editors), Degenerate Diffusions. The IMA Volumes in Mathematics and its Applications, Vol. 47, pp. 1960.CrossRefGoogle Scholar
Bosch, J., Klamt, S. & Stoll, M. (2018) Generalizing diffuse interface methods on graphs: non-smooth potentials and hypergraphs. SIAM J. Appl. Math. 78(3), 13501377.CrossRefGoogle Scholar
Boyd, S. & Vandenberghe, L. (2004) Convex Optimization, Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
Brezis, H. (2010) Functional Analysis, Sobolev Spaces and Partial Differential Equations, Springer, Berlin.Google Scholar
Bronsard, L. & Kohn, R. V. (1991) Motion by mean curvature as the singular limit of Ginzburg–Landau dynamics. J. Differ. Equations 90, 211237.CrossRefGoogle Scholar
Budd, J. & van Gennip, Y. (2020) Graph Merriman–Bence–Osher as a SemiDiscrete implicit Euler scheme for graph Allen–Cahn flow. SIAM J. Math. Anal. 52(5), 41014139.CrossRefGoogle Scholar
Budd, J., van Gennip, Y. & Latz, J. (2021) Classification and image processing with a semi-discrete scheme for fidelity forced Allen–Cahn on graphs. GAMM Mitteilungen 44(1), e202100004.CrossRefGoogle Scholar
Cahn, J. W. (1964) On spinodal decomposition. Acta Metall. 9, 795801.CrossRefGoogle Scholar
Cahn, J. W. & Hilliard, J. E. (1958) Free energy of a nonuniform system. I. Interfacial free energy. J. Chem. Phys. 28(2), 258267.CrossRefGoogle Scholar
Chen, X. & Elliott, C. M. (1994) Asymptotics for a parabolic double obstacle problem. Proc. R. Soc. Lond. A 444, 429445.Google Scholar
Chen, X., Hilhorst, D. & Logak, E. (2010) Mass conserving Allen–Cahn equation and volume preserving mean curvature flow. Interfaces Free Boundaries 12, 527549.CrossRefGoogle Scholar
Cucuringu, M., Pizzoferrato, A. & van Gennip, Y. (2021) An MBO scheme for clustering and semi-supervised clustering of signed networks. Communications in Mathematical Sciences 19(1), 73109.CrossRefGoogle Scholar
Desai, M. & Rao, V. (1994) A characterization of the smallest eigenvalue of a graph. J. Graph Theory 18(2), 181194.CrossRefGoogle Scholar
Evans, L. C. (1993) Convergence of an algorithm for mean curvature motion. Indiana Univ. Math. J. 42, 533557.CrossRefGoogle Scholar
Haemers, W. H. & Spence, E. (2004) Enumeration of cospectral graphs. Eur. J. Comb. 25(2), 199211.CrossRefGoogle Scholar
Haeseler, S., Keller, M., Lenz, D. & Wojciechowski, R. K. (2012) Laplacians on infinite graphs: Dirichlet and Neumann boundary conditions. J. Spectr. Theory 2(4), 397432.CrossRefGoogle Scholar
Hein, M., Audibert, J.-Y. & von Luxburg, U. (2005) From graphs to manifolds—weak and strong pointwise consistency of graph. In: Auer, P. and Meir, R. (editors), Proceedings of the 18th Annual Conference on Learning Theory (COLT), Springer, New York, pp. 470485.Google Scholar
Jacobs, M., Merkurjev, E. & Esedoḡlu, S. (2018) Auction dynamics: a volume constrained MBO scheme. J. Comp. Phys. 354, 288310.CrossRefGoogle Scholar
Juditsky, A. Convex Optimization Lecture 1, available online at https://www-ljk.imag.fr/membres/ Anatoli.Iouditski/cours/convex/chapitre_1.pdf accessed 26/05/2020.Google Scholar
Laux, T. & Swartz, D. (2016) Convergence of thresholding schemes incorporating bulk effects. arXiv preprint arXiv:1601.02467.Google Scholar
Luo, X. & Bertozzi, A. L. (2017) Convergence analysis of the graph Allen–Cahn scheme. J. Stat. Phys. 167(3), 934958.CrossRefGoogle Scholar
Merkurjev, E., Garcia, C., Bertozzi, A. L., Flenner, A. & Percus, A. (2014) Diffuse interface methods for multiclass segmentation of high-dimensional data. Appl. Math. Lett. 33, 2934.CrossRefGoogle Scholar
Merkurjev, E., Kostić, T. & Bertozzi, A. L. (2013) An MBO scheme on graphs for segmentation and image processing. SIAM J. Imaging Sci. 6(4), 19031930.CrossRefGoogle Scholar
Mohar, B. (1991) The Laplacian spectrum of graphs. In: Alavi, Y., Chartrand, G., Oellermann, O. R. & Schwenk, A. J. (editors), Graph Theory, Combinatorics, and Applications, Vol. 2, Wiley, pp. 871898.Google Scholar
Mugnai, L., Seis, C. & Spadaro, E. (2015) Global solutions to the volume-preserving mean-curvature flow. arXiv preprint arXiv:1502.07232.Google Scholar
Rubinstein, J. & Sternberg, P. (1992) Nonlocal reaction-diffusion equations and nucleation. IMA J. Appl. Math. 48, 249264.Google Scholar
Rudin, W. (1991) Functional Analysis, McGraw-Hill, New York.Google Scholar
Ruuth, S. J. & Wetton, B. (2003) A simple scheme for volume-preserving motion by mean curvature. J. Sci. Comput. 19(1), 373384.Google Scholar
van Gennip, Y. (2020) An MBO scheme for minimizing the graph Ohta–Kawasaki functional. J. Nonlinear Sci. 30, 23252373.Google Scholar
van Gennip, Y., Guillen, N., Osting, B. & Bertozzi, A. L. (2014) Mean curvature, threshold dynamics, and phase field theory on finite graphs. Milan J. Math. 82(1), 365.Google Scholar
Zhang, Y., Zhao, Z. & Feng, Z. (2020) A unified approach to scalable spectral sparsification of directed graphs. arXiv preprint arXiv:1812.04165.Google Scholar
Zhou, D., Schölkopf, B. & Hofmann, T. (2005) Semi-supervised learning on directed graphs. In: NIPS’05 Advances in Neural Information Processing Systems, pp. 16331640.Google Scholar