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ON CLIQUES AND LAGRANGIANS OF HYPERGRAPHS

  • Tang, Qingsong (College of Sciences Northeastern University) ;
  • Zhang, Xiangde (College of Sciences Northeastern University) ;
  • Zhao, Cheng (Department of Mathematics and Computer Science Indiana State University)
  • Received : 2018.01.30
  • Accepted : 2018.09.19
  • Published : 2019.05.31

Abstract

Given a graph G, the Motzkin and Straus formulation of the maximum clique problem is the quadratic program (QP) formed from the adjacent matrix of the graph G over the standard simplex. It is well-known that the global optimum value of this QP (called Lagrangian) corresponds to the clique number of a graph. It is useful in practice if similar results hold for hypergraphs. In this paper, we attempt to explore the relationship between the Lagrangian of a hypergraph and the order of its maximum cliques when the number of edges is in a certain range. Specifically, we obtain upper bounds for the Lagrangian of a hypergraph when the number of edges is in a certain range. These results further support a conjecture introduced by Y. Peng and C. Zhao (2012) and extend a result of J. Talbot (2002). We also establish an upper bound of the clique number in terms of Lagrangians for hypergraphs.

Keywords

E1BMAX_2019_v56n3_569_f0001.png 이미지

Figure 1. Hessian Diagram on [t](r)

References

  1. I. M. Bomze, Evolution towards the maximum clique, J. Global Optim. 10 (1997), no. 2, 143-164. https://doi.org/10.1023/A:1008230200610
  2. M. Budinich, Exact bounds on the order of the maximum clique of a graph, Discrete Appl. Math. 127 (2003), no. 3, 535-543. https://doi.org/10.1016/S0166-218X(02)00386-4
  3. S. Busygin, A new trust region technique for the maximum weight clique problem, Discrete Appl. Math. 154 (2006), no. 15, 2080-2096. https://doi.org/10.1016/j.dam.2005.04.010
  4. A. Chakraborty and S. Ghosh, Clustering hypergraphs for discovery of overlapping communities in folksonomies, in Dynamics on and of complex networks. Vol. 2, 201-220, Model. Simul. Sci. Eng. Technol, Birkhauser/Springer, New York, 2013.
  5. P. Frankl and Z. Furedi, Extremal problems whose solutions are the blowups of the small Witt-designs, J. Combin. Theory Ser. A 52 (1989), no. 1, 129-147. https://doi.org/10.1016/0097-3165(89)90067-8
  6. P. Frankl and V. Rodl, Hypergraphs do not jump, Combinatorica 4 (1984), no. 2-3, 149-159. https://doi.org/10.1007/BF02579215
  7. L. E. Gibbons, D. W. Hearn, P. M. Pardalos, and M. V. Ramana, Continuous characterizations of the maximum clique problem, Math. Oper. Res. 22 (1997), no. 3, 754-768. https://doi.org/10.1287/moor.22.3.754
  8. D. Hefetz and P. Keevash, A hypergraph Turan theorem via Lagrangians of intersecting families, J. Combin. Theory Ser. A 120 (2013), no. 8, 2020-2038. https://doi.org/10.1016/j.jcta.2013.07.011
  9. H. Liu, J. Latecki, and S. Yan, Robust clustering as ensembles of affinity relations, Advances in Neural Information Processing Systems (2010), 1414-1422.
  10. T. S. Motzkin and E. G. Straus, Maxima for graphs and a new proof of a theorem of Turan, Canad. J. Math. 17 (1965), 533-540. https://doi.org/10.4153/CJM-1965-053-6
  11. D. Mubayi, A hypergraph extension of Turan's theorem, J. Combin. Theory Ser. B 96 (2006), no. 1, 122-134. https://doi.org/10.1016/j.jctb.2005.06.013
  12. P. M. Pardalos and A. T. Phillips, A global optimization approach for solving the maximum clique problem, Int. J. Comput. Math. 33 (1990), 209-216. https://doi.org/10.1080/00207169008803851
  13. M. Pavan and M. Pelillo, Generalizing the motzkin-straus theorem to edge-weighted graphs, with applications to image segmentation, Lecture Notes in Computer Science 2683 (2003), 485-500.
  14. M. Pavan and M. Pelillo, Dominant sets and pairwise clustering, IEEE Trans. Pattern Anal. Mach. Intell. 29 (2007), 167-172. https://doi.org/10.1109/TPAMI.2007.250608
  15. Y. Peng, Q. Tang, and C. Zhao, On Lagrangians of r-uniform hypergraphs, J. Comb. Optim. 30 (2015), no. 3, 812-825. https://doi.org/10.1007/s10878-013-9671-3
  16. Y. Peng and C. Zhao, A Motzkin-Straus type result for 3-uniform hypergraphs, Graphs Combin. 29 (2013), no. 3, 681-694. https://doi.org/10.1007/s00373-012-1135-5
  17. S. Rota Bulo, A continuous characterization of maximal cliques in k-uniform hypergraphs. in Learning and Intellig. Optim. (Lecture Notes in Computer Science), 5313 (2008), 220-233.
  18. S. Rota Bulo and M. Pelillo, A generalization of the Motzkin-Straus theorem to hypergraphs, Optim. Lett. 3 (2009), no. 2, 287-295. https://doi.org/10.1007/s11590-008-0108-3
  19. S. Rota Bulo and M. Pelillo, A game-theoretic approach to hypergraph clustering, IEEE Trans. Pattern Anal. Mach. Intell. 35 (2013), 1312-1327. https://doi.org/10.1109/TPAMI.2012.226
  20. S. Rota Bulo, A. Torsello, and M. Pelillo, A continuous-based approach for partial clique enumeration, Graph-Based Representations Patt. Recogn. 4538 (2007), 61-70.
  21. A. F. Sidorenko, On the maximal number of edges in a homogeneous hypergraph that does not contain prohibited subgraphs, Mat. Zametki 41 (1987), no. 3, 433-455, 459.
  22. V. T. Sos and E. G. Straus, Extremals of functions on graphs with applications to graphs and hypergraphs, J. Combin. Theory Ser. B 32 (1982), no. 3, 246-257. https://doi.org/10.1016/0095-8956(82)90002-8
  23. J. Talbot, Lagrangians of hypergraphs, Combin. Probab. Comput. 11 (2002), no. 2, 199-216. https://doi.org/10.1017/S0963548301005053
  24. Q. S. Tang, Y. Peng, X. D. Zhang, and C. Zhao, Some results on Lagrangians of hypergraphs, Discrete Appl. Math. 166 (2014), 222-238. https://doi.org/10.1016/j.dam.2013.09.023
  25. Q. S. Tang, Y. Peng, X. D. Zhang, and C. Zhao, Connection between the clique number and the Lagrangian of 3-uniform hypergraphs, Optim. Lett. 10 (2016), no. 4, 685-697. https://doi.org/10.1007/s11590-015-0907-2
  26. M. Tyomkyn, Lagrangians of hypergraphs: the Frankl-Furedi conjecture holds almost everywhere, J. Lond. Math. Soc. (2) 96 (2017), no. 3, 584-600. https://doi.org/10.1112/jlms.12082
  27. Y. Zhao, Q. Chen,S. Yan, D. Zhang, and T. Chua, Community understanding in location-based social networks, Human-Centered Social Media Analytics (2014), 43-74.