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An optimal path finding strategy in networks based on random walk

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Abstract

In this paper, we study a path finding strategy based on random walk in which we allow multiple particles to set out from different but neighboring sources to their common destination. Three path finding models, the single-particle-form-one-source, the multiple-particles-from-one-source, and the last multiple-particles-form-multiple-sources (MSMP) are described. Then we apply the three models to different simulation networks. The experiment results show that the MSMP schema can decrease the path finding cost. Furthermore, we propose an absorption strategy to deal with the additional Brownian particles in networks. The experiment results on BA networks show that the absorption strategy can increase the probability of a successful path finding. In the end, we find he path found out by above methods may be the shortest theoretically, but may not be optimal in the practical application. To overcome this, we put forward a method to calculate the optimal path based on arrival reliability and verify its correctness by enumeration.

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Acknowledgments

The work described in this paper was supported by the Fundamental Research Funds for the Central Universities (KYZ201669, Y0201600022) and Natural Science Foundation of China (Grant Nos. 51278101 and 51578149).

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China

    Bin Hu & Huan-yan Qian

  2. College of Information Science and Technology, Nanjing Agricultural University, Nanjing, China

    Bin Hu, Yi Shen & Jia-xing Yan

  3. School of Transportation, Southeast University, Nanjing, China

    Yi Shen

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  1. Bin Hu
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  2. Huan-yan Qian
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  3. Yi Shen
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  4. Jia-xing Yan
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Correspondence to Bin Hu.

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Hu, B., Qian, Hy., Shen, Y. et al. An optimal path finding strategy in networks based on random walk. Cluster Comput 19, 2179–2188 (2016). https://doi.org/10.1007/s10586-016-0674-6

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  • DOI: https://doi.org/10.1007/s10586-016-0674-6

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