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Improving the Performance of an Integer Linear Programming Community Detection Algorithm Through Clique Filtering

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Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11619))

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Abstract

Different fields of science use network representation as a framework to model their systems. The analysis of network structure can give us essential information about the system. However, the size of such a network can limit the applicability of some fundamental techniques like mathematical programming. Thus, here we propose a novel network size reduction technique based on a clique filtering approach. Our goal is twofold: (1) reduce the network size and speed up the community detection process, and (2) preserve the modularity of the original partition in the context of the exact model. Conducted experiments show the feasibility and correctness of the proposed technique.

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References

  1. Agarwal, G., Kempe, D.: Modularity-maximizing graph communities via mathematical programming. Eur. Phys. J. B 66(3), 409–418 (2008)

    Article  MathSciNet  Google Scholar 

  2. Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Modern Phys. 74(1), 47 (2002). https://doi.org/10.1103/RevModPhys.74.47

    Article  MathSciNet  MATH  Google Scholar 

  3. Arenas, A., Duch, J., Fernández, A., Gómez, S.: Size reduction of complex networks preserving modularity. New J. Phys. 9(6), 176 (2007). https://doi.org/10.1088/1367-2630/9/6/176

    Article  MathSciNet  Google Scholar 

  4. Bonchi, F., Morales, G.D.F., Gionis, A., Ukkonen, A.: Activity preserving graph simplification. Data Min. Knowl. Discov. 27(3), 321–343 (2013)

    Article  MathSciNet  Google Scholar 

  5. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  6. Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)

    Article  MathSciNet  Google Scholar 

  7. Gemmetto, V., Cardillo, A., Garlaschelli, D.: Irreducible network backbones: unbiased graph filtering via maximum entropy. arXiv preprint arXiv:1706.00230 (2017)

  8. Gionis, A., Rozenshtein, P., Tatti, N., Terzi, E.: Community-aware network sparsification. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 426–434. SIAM (2017)

    Chapter  Google Scholar 

  9. IBM: IBM ILOG CPLEX 12.7.1 (1987–2017)

    Google Scholar 

  10. Kim, J.R., Kim, J., Kwon, Y.K., Lee, H.Y., Heslop-Harrison, P., Cho, K.H.: Reduction of complex signaling networks to a representative kernel. Sci. Signal. 4(175), ra35–ra35 (2011)

    Article  Google Scholar 

  11. Miyauchi, A., Sukegawa, N.: Redundant constraints in the standard formulation for the clique partitioning problem. Optim. Lett. 9(1), 199–207 (2015)

    Article  MathSciNet  Google Scholar 

  12. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004). https://doi.org/10.1103/PhysRevE.69.026113

    Article  Google Scholar 

  13. Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003). https://doi.org/10.1137/S003614450342480

    Article  MathSciNet  MATH  Google Scholar 

  14. Newman, M.E.: Analysis of weighted networks. Phys. Rev. E 70(5), 056131 (2004)

    Article  Google Scholar 

  15. Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006). https://doi.org/10.1073/pnas.0601602103

    Article  Google Scholar 

  16. Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (2015). http://networkrepository.com

  17. Stanley, N., Kwitt, R., Niethammer, M., Mucha, P.J.: Compressing networks with super nodes. CoRR abs/1706.04110 (2017). http://arxiv.org/abs/1706.04110

  18. Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001). https://doi.org/10.1038/35065725

    Article  MATH  Google Scholar 

  19. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998). https://doi.org/10.1038/30918

    Article  MATH  Google Scholar 

  20. Xiao, Y., MacArthur, B.D., Wang, H., Xiong, M., Wang, W.: Network quotients: structural skeletons of complex systems. Phys. Rev. E 78(4), 046102 (2008)

    Article  Google Scholar 

Download references

Acknowledgment

M.G.Q. acknowledges the support by São Paulo Research Foundation (FAPESP, Proc. 2011/18496-7 & 2015/50122-0) and by the the Brazilian National Research Council (CNPq Proc. 310908/2015-9 & 434886/2018-1). L.A.N.L. acknowledges the support by (CNPq Proc. 301836/2014-0) and L.H.N.L. acknowledges the support by Coordination of Superior Level Staff Improvement (CAPES).

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Authors and Affiliations

  1. Institute of Science and Technology, Federal University of São Paulo (UNIFESP), São José dos Campos, SP, Brazil

    Luiz Henrique Nogueira Lorena & Marcos Gonçalves Quiles

  2. National Institute for Space Research (INPE), São José dos Campos, SP, Brazil

    Luiz Antonio Nogueira Lorena

Authors
  1. Luiz Henrique Nogueira Lorena
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  2. Marcos Gonçalves Quiles
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  3. Luiz Antonio Nogueira Lorena
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Corresponding author

Correspondence to Marcos Gonçalves Quiles .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Lorena, L.H.N., Quiles, M.G., Lorena, L.A.N. (2019). Improving the Performance of an Integer Linear Programming Community Detection Algorithm Through Clique Filtering. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11619. Springer, Cham. https://doi.org/10.1007/978-3-030-24289-3_56

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  • DOI: https://doi.org/10.1007/978-3-030-24289-3_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24288-6

  • Online ISBN: 978-3-030-24289-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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