Research on algorithm for solving maximum independent set of semi-external data of large graph data

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The maximum independent set algorithm for large-scale data semi-existing data is studied and the solving method of the largest independent set problem in large-map data is mainly analyzed in this paper. The specific research contents are mainly divided into semi-external map algorithm based on Greedy heuristic strategy, semi-external map algorithm based on swap and design, and implementation of semi-external graph algorithm processing function library. Experiments on a large number of real and artificially generated data sets show that the algorithm in this paper is very efficient both in time and in space. The largest independent set obtained by the algorithm can reach more than 96% of its theoretical upper bound for most of the data.

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  1. 1.

    Peter AHG, Rocha M, Bullock JS et al (2018) Cosmological simulations with self-interacting dark matter—II. Halo shapes versus observations. Mon Not R Astron Soc 430(1):105–120

  2. 2.

    Oliver S, Frost M, Farrah D et al (2018) Specific star formation and the relation to stellar mass from 0 < z<2 as seen in the far-infrared at 70 and 160 μm. Mon Not R Astron Soc 405(4):2279–2294

  3. 3.

    Wyithe JSB, Webster RL, Turner EL (2018) A measurement of the transverse velocity of Q2237 + 0305. Mon Not R Astron Soc 309(1):261–272

  4. 4.

    Hahn O, Abel T (2018) Multi-scale initial conditions for cosmological simulations. Mon Not R Astron Soc 415(3):2101–2121

  5. 5.

    Efstathiou G, Gratton S, Paci F (2018) Impact of galactic polarized emission on B-mode detection at low multipoles. Mon Not R Astron Soc 397(3):1355–1373

  6. 6.

    Lou Y, Yi C (2018) Self-similar dynamics of a relativistically hot gas. Mon Not R Astron Soc 384(2):611–629

  7. 7.

    Driver SP, Robotham ASG (2018) Quantifying cosmic variance. Mon Not R Astron Soc 407(4):2131–2140

  8. 8.

    Freeman PE, Izbicki R, Lee AB et al (2018) New image statistics for detecting disturbed galaxy morphologies at high redshift. Mon Not R Astron Soc 434(1):282–295

  9. 9.

    Austin TM, Brezina M, Jamroz B et al (2012) Semi-automatic sparse preconditioners for high-order finite element methods on non-uniform meshes. J Comput Phys 231(14):4694–4708

  10. 10.

    Zhang J, Wang YP (2014) A method for inversion of periodic open boundary conditions in two-dimensional tidal models. Comput Methods Appl Mech Eng 275(13):20–38

  11. 11.

    Polozov O, Gulwani S (2015). FlashMeta: a framework for inductive program synthesis. In: ACM sigplan international conference on object-oriented programming, systems, languages, and applications. ACM, pp 107–126

  12. 12.

    Ghosh A, Koopmans EVOL, Chapman E et al (2015) A Bayesian analysis of redshifted 21-cm H I signal and foregrounds: simulations for LOFAR. Mon Not R Astron Soc 452(2):1587

  13. 13.

    Mcmahan HB (2015) A survey of algorithms and analysis for adaptive online learning. J Mach Learn Res 18:1–50

  14. 14.

    Damos P, Soulopoulou P (2015) Correction: Do insect populations die at constant rates as they become older? Contrasting demographic failure kinetics with respect to temperature according to the Weibull model. PLoS ONE 10(8):e0127328

  15. 15.

    Brewin RJW, Sathyendranath S, Müller D et al (2015) The ocean colour climate change initiative: III. A round-robin comparison on in-water bio-optical algorithms. Remote Sens Environ 162:271–294

  16. 16.

    Grinshpoun T, Meisels A (2014) Completeness and performance of the APO algorithm. J Artif Intell Res 33(33):223–258

  17. 17.

    Lin C, Makis V (2015) A comparison of hidden Markov and SEMI-Markov modeling for a deterioration system subject to vibration monitoring. Int J Perform En 11(3):213–228

  18. 18.

    Nassirtoussi AK, Aghabozorgi S, Wah TY et al (2014) Text mining for market prediction: a systematic review. Expert Syst Appl 41(16):7653–7670

  19. 19.

    Rollett AD, Lee SB, Campman R et al (2014) Three-dimensional characterization of microstructure by electron back-scatter diffraction. Annu Rev Mater Sci 37(37):627–658

  20. 20.

    Needell D, Tropp JA (2014) Paved with good intentions: analysis of a randomized block Kaczmarz method. Linear Algebra Appl 441(1):199–221

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This work was supported by Chongqing Big Data Engineering Laboratory for Children, Chongqing Electronics Engineering Technology Research Center for Interactive Learning, the Science and Technology Research Project of Chongqing Municipal Education Commission of China (No. KJ1601401), the Science and Technology Research Project of Chongqing University of Education (No. KY201725C), Basic Research and Frontier Exploration of Chongqing Science and Technology Commission (CSTC2014jcyjA40019), Project of Science and Technology Research Program of Chongqing Education Commission of China (No. KJZD-K201801601).

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Correspondence to Fangcheng He.

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Wei, P., He, F., Shang, C. et al. Research on algorithm for solving maximum independent set of semi-external data of large graph data. Neural Comput & Applic 32, 85–91 (2020).

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  • Large image data
  • Semi-external storage
  • Independent set