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

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Abstract

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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Acknowledgements

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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Authors and Affiliations

  1. School of Mathematics and Information Engineering, Chongqing University of Education, Chongqing, China

    Pengcheng Wei, Chuanfu Shang & Jing Li

  2. School of Foreign Languages and Literatures, Chongqing University of Education, Chongqing, China

    Fangcheng He

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  1. Pengcheng Wei
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  2. Fangcheng He
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  3. Chuanfu Shang
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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). https://doi.org/10.1007/s00521-018-3779-4

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  • DOI: https://doi.org/10.1007/s00521-018-3779-4

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