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From Complex Network to Skeleton: \( \varvec{m}_{\varvec{j}} \)-Modified Topology Potential for Node Importance Identification

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Advanced Data Mining and Applications (ADMA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11323))

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Abstract

Node importance identification is a crucial content in studying the substantial information and the inherent behaviors of complex network. On the basis of topological characteristics of nodes in complex network, we introduce the idea of topology potential from data field theory to capture the important nodes and view it as the skeleton nodes. Inspired by an assumption that different mass of node (\( m_{j} \) parameter) reflects different quality and interaction reliability over the network space. We propose TP-KS method that is an improved topology potential algorithm whose \( m_{j} \) is identified by k-shell centrality. The important nodes identified by TP-KS is ranked and verified by SIR epidemic spreading model. Through the theoretical and experimental analysis, it is proved that TP-KS can effectively extract the importance of nodes in complex network. The better results from TP-KS are also confirmed in both real-world networks and artificial random scale-free networks.

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Acknowledgement

This work was supported by the National Key Research and Development Program of China (No. 2016YFB0502600), The National Natural Science Fund of China (61472039), Beijing Institute of Technology International Cooperation Project (GZ2016085103), and Open Fund of Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation (2017NGCMZD03).

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, No. 5 South Zhong Guan Cun Street, Beijing, 100081, People’s Republic of China

    Hanning Yuan, Kanokwan Malang & Yuanyuan Lv

  2. International College of Digital Innovation, Chiang Mai University, No. 239 Nimmanhaemin Road, Suthep, Muang, Chiang Mai, 50200, Thailand

    Aniwat Phaphuangwittayakul

Authors
  1. Hanning Yuan
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  2. Kanokwan Malang
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  3. Yuanyuan Lv
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  4. Aniwat Phaphuangwittayakul
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Corresponding author

Correspondence to Kanokwan Malang .

Editor information

Editors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Guojun Gan

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  3. The University of Queensland, Brisbane, QLD, Australia

    Xue Li

  4. Beijing Institute of Technology, Beijing, China

    Shuliang Wang

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Yuan, H., Malang, K., Lv, Y., Phaphuangwittayakul, A. (2018). From Complex Network to Skeleton: \( \varvec{m}_{\varvec{j}} \)-Modified Topology Potential for Node Importance Identification. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_35

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  • DOI: https://doi.org/10.1007/978-3-030-05090-0_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05089-4

  • Online ISBN: 978-3-030-05090-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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