Skip to main content
Log in

Cheeger’s cut, maxcut and the spectral theory of 1-Laplacian on graphs

  • Articles
  • Published:
Science China Mathematics Aims and scope Submit manuscript

Abstract

This is primarily an expository paper surveying up-to-date known results on the spectral theory of 1-Laplacian on graphs and its applications to the Cheeger cut, maxcut and multi-cut problems. The structure of eigenspace, nodal domains, multiplicities of eigenvalues, and algorithms for graph cuts are collected.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Bauer F, Jost J. Bipartite and neighborhood graphs and the spectrum of the normalized graph Laplace operator. Comm Anal Geom, 2013, 21: 787–845

    Article  MATH  MathSciNet  Google Scholar 

  2. Biyikoglu T, Leydold J, Stadler P F. Laplacian Eigenvectors of Graphs: Perron-Frobenius and Faber-Krahn Type Theorems. Berlin: Springer, 2007

    Book  MATH  Google Scholar 

  3. Burer S, Monteiro R D C, Zhang Y. Rank-two relaxation heuristics for MAX-CUT and other binary quadratic programs. SIAM J Optim, 2001, 12: 503–521

    Article  MATH  MathSciNet  Google Scholar 

  4. Bühler T, Rangapuram S S, Setzer S, et al. Constrained fractional set programs and their application in local clustering and community detection. In: Proceedings of the 30th International Conference on Machine Learning. Atlanta: International Machine Learning Society, 2013, 624–632

    Google Scholar 

  5. Chang K C. Variational methods for non-differentiable functionals and their applications to partial differential equations. J Math Anal Appl, 1981, 80: 102–129

    Article  MATH  MathSciNet  Google Scholar 

  6. Chang K C. Spectrum of the 1-Laplacian and Cheeger’s constant on graphs. J Graph Theor, 2016, 81: 167–207

    Article  MATH  MathSciNet  Google Scholar 

  7. Chang K C, Shao S, Zhang D. The 1-Laplacian Cheeger cut: Theory and algorithms. J Comput Math, 2015, 33: 443–467

    Article  MATH  MathSciNet  Google Scholar 

  8. Chang K C, Shao S, Zhang D. Spectrum of the signless 1-Laplacian and the dual Cheeger constant on graphs. ArXiv: 1607.00489, 2016

    Google Scholar 

  9. Chang K C, Shao S, Zhang D. Nodal domains of eigenvectors for 1-Laplacian on graphs. Adv Math, 2017, 308: 529–574

    Article  MATH  MathSciNet  Google Scholar 

  10. Cheeger J. A lower bound for the smallest eigenvalue of the Laplacian. In: Problems in Analysis. Princeton: Princeton University Press, 1970, 195–199

    Google Scholar 

  11. Chung F R K. Spectral Graph Theory. Providence: Amer Math Soc, 1997

    MATH  Google Scholar 

  12. Delorme C, Poljak S. Laplacian eigenvalues and the maximum cut problem. Math Program, 1993, 62: 557–574

    Article  MATH  MathSciNet  Google Scholar 

  13. Goemans M X, Williamson D P. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J ACM, 1995, 42: 1115–1145

    Article  MATH  MathSciNet  Google Scholar 

  14. Grippo L, Palagi L, Piccialli V. An unconstrained minimization method for solving low-rank SDP relaxations of the maxcut problem. Math Program, 2011, 126: 119–146

    Article  MATH  MathSciNet  Google Scholar 

  15. Haeseler S, Keller M, Lenz D, et al. Laplacians on infinite graphs: Dirichlet and Neumann boundary conditions. J Spectr Theory, 2012, 2: 397–432

    Article  MATH  MathSciNet  Google Scholar 

  16. Hagen L, Kahng A B. New spectral methods for ratio cut partitioning and clustering. IEEE Trans Comput-Aided Des Integr Circuits Syst, 1992, 11: 1074–1085

    Article  Google Scholar 

  17. Hein M, Bühler T. An inverse power method for nonlinear eigenproblems with applications in 1-spectral clustering and sparse PCA. Adv Neural Inf Process Syst, 2010, 23: 847–855

    Google Scholar 

  18. Lee J R, Gharan S O, Trevisan L. Multi-way spectral partitioning and higher-order Cheeger inequalities. ArXiv:1111.1055v6, 2011

    MATH  Google Scholar 

  19. Lovász L. Submodular functions and convexity. In: Mathematical Programming. The State of the Art. Berlin: Springer, 1983, 235–257

    Chapter  Google Scholar 

  20. Jain A K, Murty M N, Flynn P J. Data clustering: A review. ACM Comput Surv, 1999, 31: 264–323

    Article  Google Scholar 

  21. Jiang B, Dai Y-H. A framework of constraint preserving update schemes for optimization on Stiefel manifold. Math Program, 2015, 153: 535–575

    Article  MATH  MathSciNet  Google Scholar 

  22. Karp R M. Reducibility among combinatorial problems. In: Complexity of Computer Computations. Berlin: Springer, 1972, 85–103

    Chapter  Google Scholar 

  23. Kolmogorov V, Zabih R. What energy functions can be minimized via graph cuts? IEEE Trans Pattern Anal Mach Intell, 2004, 26: 147–159

    Article  MATH  Google Scholar 

  24. Martí R, Duarte A, Laguna M. Advanced scatter search for the Max-Cut problem. INFORMS J Comput, 2009, 21: 26–38

    Article  MATH  MathSciNet  Google Scholar 

  25. Poljak S, Rendl F. Solving the max-cut problem using eigenvalues. Discrete Appl Math, 1995, 62: 249–278

    Article  MATH  MathSciNet  Google Scholar 

  26. Shi J B, Malik J. Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell, 2000, 22: 888–905

    Article  Google Scholar 

  27. Szlam A, Bresson X. Total variation and Cheeger cuts. In: Proceedings of the 27th International Conference on Machine Learning. Haifa: International Machine Learning Society, 2010, 1039–1046

    Google Scholar 

  28. Trevisan L. Max cut and the smallest eigenvalue. SIAM J Comput, 2012, 41: 1769–1786

    Article  MATH  MathSciNet  Google Scholar 

  29. von Luxburg U. A tutorial on spectral clustering. Stat Comput, 2007, 17: 395–416

    Article  MathSciNet  Google Scholar 

  30. Wen Z, Yin W. A feasible method for optimization with orthogonality constraints. Math Program, 2013, 142: 397–434

    Article  MATH  MathSciNet  Google Scholar 

  31. Yen J Y. An algorithm for finding shortest routes from all source nodes to a given destination in general networks. Quart Appl Math, 1970, 27: 526–530

    Article  MATH  MathSciNet  Google Scholar 

  32. Zhang D. Some remarks on the 1-Laplacian and Cheeger cut. Oberwolfach Rep, 2015, 12: 442–445

    Google Scholar 

  33. Zhang D. Topological multiplicity of the maximal eigenvalue of graph 1-Laplacian. Discrete Math, 2017, http://dx.doi.org/10.1016/j.disc.2017.06.020

    Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 11371038, 11471025, 11421101 and 61121002).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to KungChing Chang.

Additional information

In honor of Professor CHENG MinDe

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, K., Shao, S. & Zhang, D. Cheeger’s cut, maxcut and the spectral theory of 1-Laplacian on graphs. Sci. China Math. 60, 1963–1980 (2017). https://doi.org/10.1007/s11425-017-9096-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11425-017-9096-6

Keywords

MSC(2010)

Navigation