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

  • Hanning Yuan
  • Kanokwan MalangEmail author
  • Yuanyuan Lv
  • Aniwat Phaphuangwittayakul
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)


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.


Complex network Skeleton network Topology potential Node importance evaluation 



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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hanning Yuan
    • 1
  • Kanokwan Malang
    • 1
    Email author
  • Yuanyuan Lv
    • 1
  • Aniwat Phaphuangwittayakul
    • 2
  1. 1.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingPeople’s Republic of China
  2. 2.International College of Digital InnovationChiang Mai UniversityChiang MaiThailand

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