Arabian Journal for Science and Engineering

, Volume 39, Issue 10, pp 7003–7011 | Cite as

Maximum Independent Set Approximation Based on Bellman-Ford Algorithm

Research Article - Computer Engineering and Computer Science
  • 147 Downloads

Abstract

This paper presents a new algorithm for approximating the solution of the maximum independent set (MIS) problem. The proposed algorithm takes a novel approach of treating the MIS problem as a least-cost path problem, using an adapted version of Bellman–Ford algorithm, in which all vertices are used as sources, and the cost of a vertex is measured as the number of vertices excluded if this vertex is included in the independent set. Analysis of the proposed algorithm shows that its running time is approximately O(n(n2m)), where n is the number of vertices and m is the number of edges. Extensive tests against several benchmark graphs and random-generated graphs show a significant improvement of the proposed algorithm over other approximation algorithms, including one of the best known ones such as Greedy algorithm.

Keywords

Maximum independent set Combinatorial problems Approximation algorithms Graph algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Garey, M.R.; Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York, NY, USA (1979). http://dl.acm.org/citation.cfm?id=578533
  2. 2.
    Engebretsen, L.; Holmerin, J.: Clique is hard to approximate within n 1-o(1). In: Montanari, U.; Rolim, J.D.P.; Welzl, E. (eds.) Automata, Languages and Programming. Lecture Notes in Computer Science, no. 1853, pp. 2–12. Springer, Berlin Heidelberg (2000)Google Scholar
  3. 3.
    Agarwal, P.K.; van Kreveld, M.; Suri, S.: Label placement by maximum independent set in rectangles. Comput. Geom. 11(3–4), 209–218 (1998)CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Strijk, T.; Verweij, B.; Aardal, K.: Algorithms for maximum independent set applied to map labelling. Department of Computer Science, Utrecht University (2000)Google Scholar
  5. 5.
    Luo, H., Lu, S., Bharghavan, V.: A new model for packet scheduling in multihop wireless networks. In: Proceedings of the 6th Annual International Conference on Mobile Computing and Networking. MobiCom’00, pp. 76–86. ACM, New York (2000)Google Scholar
  6. 6.
    Wan, P.-J.;Frieder, O.; Jia, X.; Yao, F.; Xu, X.; Tang, S.: Wireless link scheduling under physical interference model. In: 2011 Proceedings IEEE INFOCOM, pp. 838–845 (2011)Google Scholar
  7. 7.
    Zaguia, N.; Stojmenovic, I.; Daadaa, Y.: Simplified bluetooth scatternet formation using maximal independent sets. In: International Conference on Complex, Intelligent and Software Intensive Systems, 2008. CISIS 2008, pp. 443–448 (2008)Google Scholar
  8. 8.
    Brendel, W.; Amer, M.; Todorovic, S.: Multiobject tracking as maximum weight independent set. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1273–1280 (2011)Google Scholar
  9. 9.
    Pattillo, J.; Butenko, S.: Maximum clique, maximum independent set, and graph coloring problems. Wiley Encyclopedia of Operations Research and Management Science (2010)Google Scholar
  10. 10.
    Bourgeois, N.; Escoffier, B.; Paschos, V.T.: An O *(1.0977n) exact algorithm for max independent set in sparse graphs. In: Grohe, M.; Niedermeier, R. (eds.) Parameterized and Exact Computation. Lecture Notes in Computer Science, Springer, no. 5018, pp. 55–65., Berlin Heidelberg (2008)Google Scholar
  11. 11.
    Xiao, M.; Nagamochi, H.: An exact algorithm for maximum independent set in degree-5 graphs. In: Fellows, M.; Tan, X.; Zhu, B.: (eds.) Frontiers in Algorithmics and Algorithmic Aspects in Information and Management. Lecture Notes in Computer Science, no. 7924, pp. 72–83. Springer, Berlin Heidelberg (2013)Google Scholar
  12. 12.
    Selkow, S.M.: A probabilistic lower bound on the independence number of graphs. Discrete Math. 132(1–3), 363–365 (1994)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Halldórsson, M.M.; Radhakrishnan, J.: Greed is good: approximating independent sets in sparse and bounded-degree graphs. Algorithmica 18(1), 145–163 (1997)CrossRefGoogle Scholar
  14. 14.
    Goldberg, M.; Hollinger, D.; Magdon-Ismail, M.: Experimental evaluation of the greedy and random algorithms for finding independent sets in random graphs. In: Nikoletseas, S.E. (ed.) Experimental and Efficient Algorithms. Lecture Notes in Computer Science, no. 3503, pp. 513–523., Springer, Berlin Heidelberg (2005)Google Scholar
  15. 15.
    Coja-Oghlan, A.; Efthymiou, C.: On independent sets in random graphs. In: Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 136–144 (2011)Google Scholar
  16. 16.
    Borowiecki, P.; Göring, F.: GreedyMAX-type algorithms for the maximum independent set problem. In: Černá, I.; Gyimóthy, T.; Hromkovič, J.; Jefferey, K.; Králović, R.; Vukolić, M.; Wolf, S. (eds.) SOFSEM 2011: Theory and Practice of Computer Science. Lecture Notes in Computer Science, no. 6543, pp. 146–156. Springer, Berlin Heidelberg (2011)Google Scholar
  17. 17.
    Balaji, S.; Swaminathan, V.; Kannan, K.: A simple algorithm to optimize maximum independent set. Adv. Model. Optim. 12(1), 107–118 (2010)MathSciNetGoogle Scholar
  18. 18.
    Blelloch, G.E.; Fineman, J.T.; Shun, J.: Greedy sequential maximal independent set and matching are parallel on average. In: Proceedings of the 24th ACM symposium on Parallelism in Algorithms and Architectures. SPAA ’12, pp. 308–317. ACM, New York (2012)Google Scholar
  19. 19.
    Plotnikov, A.D.: Heuristic algorithm for finding the maximum independent set. Cybern. Syst. Anal. 48(5), 673–680 (2012)CrossRefMATHGoogle Scholar
  20. 20.
    Matoušek, J.; Vondrák, J.: The probabilistic method: lecture notes. Charles University. http://www.cs.cmu.edu/afs/cs.cmu.edu/academic/class/15859-f09/www/handouts/matousek-vondrak-prob-ln (2008). Accessed 22 Aug 2013
  21. 21.
    DIMACS, “DIMACS implementation challenges,”. http://dimacs.rutgers.edu/Challenges/ (2012). Accessed 22 Aug 2013
  22. 22.
    Xu, K.: Challenging benchmarks for maximum clique, maximum independent set, minimum vertex cover and vertex coloring—generating hard graph problem instances. http://www.nlsde.buaa.edu.cn/~kexu/benchmarks/graph-benchmarks.htm (2010). Accessed 22 Aug 2013
  23. 23.
    Erickson, J.: Efficient exponential-time algorithms: lecture notes. University of Illinois at Urbana-Champaign. http://www.cs.uiuc.edu/jeffe/teaching/algorithms/notes/04-fastexpo (2011). Accessed 22 Aug 2013

Copyright information

© King Fahd University of Petroleum and Minerals 2014

Authors and Affiliations

  1. 1.Department of Computer Engineering, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia

Personalised recommendations