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A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential

Author

Listed:
  • Hanning Yuan
  • Yanni Han
  • Ning Cai
  • Wei An

Abstract

Inspired by the theory of physics field, in this paper, we propose a novel backbone network compression algorithm based on topology potential. With consideration of the network connectivity and backbone compression precision, the method is flexible and efficient according to various network characteristics. Meanwhile, we define a metric named compression ratio to evaluate the performance of backbone networks, which provides an optimal extraction granularity based on the contributions of degree number and topology connectivity. We apply our method to the public available Internet AS network and Hep-th network, which are the public datasets in the field of complex network analysis. Furthermore, we compare the obtained results with the metrics of precision ratio and recall ratio. All these results show that our algorithm is superior to the compared methods. Moreover, we investigate the characteristics in terms of degree distribution and self-similarity of the extracted backbone. It is proven that the compressed backbone network has a lot of similarity properties to the original network in terms of power-law exponent.

Suggested Citation

  • Hanning Yuan & Yanni Han & Ning Cai & Wei An, 2018. "A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential," Complexity, Hindawi, vol. 2018, pages 1-8, October.
  • Handle: RePEc:hin:complx:8604132
    DOI: 10.1155/2018/8604132
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    References listed on IDEAS

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    1. Christopher E. Hutchins & Marge Benham-Hutchins, 2010. "Hiding in plain sight: criminal network analysis," Computational and Mathematical Organization Theory, Springer, vol. 16(1), pages 89-111, March.
    2. S. Scellato & A. Cardillo & V. Latora & S. Porta, 2006. "The backbone of a city," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 221-225, March.
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    Cited by:

    1. Malang, Kanokwan & Wang, Shuliang & Phaphuangwittayakul, Aniwat & Lv, Yuanyuan & Yuan, Hanning & Zhang, Xiuzhen, 2020. "Identifying influential nodes of global terrorism network: A comparison for skeleton network extraction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).

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