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Label propagation algorithm based on node importance

Author

Listed:
  • Wang, Tao
  • Chen, Shanshan
  • Wang, Xiaoxia
  • Wang, Jinfang

Abstract

Label propagation algorithm (LPA) has attracted much attention due to its linear time complexity. However, there are disadvantages of uncertainty and randomness in the label propagation process, which may affect the stability and accuracy of community detection. In order to solve this problem, this paper proposes a novel label propagation algorithm based on node importance (NI-LPA). In the algorithm, a new index of node importance is presented which integrates the signal propagation of nodes, ks value of nodes themselves and Jaccard distance between adjacent nodes. The signal propagation considers the node importance from the perspective of network locality, the index reflects the position of nodes in the entire network, and Jaccard distance embodies the connection between nodes. The proposed index can fully reflect the node importance in the entire network. In the label propagation process, when the nodes with the maximum number of neighboring nodes are not unique, their labels are updated in terms of node importance. The proposed algorithm can avoid the instability caused by random selection in LPA algorithm. Experiments on real and synthetic networks show that NI-LPA can significantly improve the modularity of community and reduce the number of iterations. NI-LPA has better stability and accuracy than LPA.

Suggested Citation

  • Wang, Tao & Chen, Shanshan & Wang, Xiaoxia & Wang, Jinfang, 2020. "Label propagation algorithm based on node importance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    • Handle: RePEc:eee:phsmap:v:551:y:2020:i:c:s0378437120300042
    DOI: 10.1016/j.physa.2020.124137
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    References listed on IDEAS

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    1. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Community detection using local neighborhood in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 665-677.
    2. Dabaghi Zarandi, Fataneh & Kuchaki Rafsanjani, Marjan, 2018. "Community detection in complex networks using structural similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 882-891.
    3. Li, Wei & Huang, Ce & Wang, Miao & Chen, Xi, 2017. "Stepping community detection algorithm based on label propagation and similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 145-155.
    4. Jin, Hong & Yu, Wei & Li, ShiJun, 2018. "A clustering algorithm for determining community structure in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 980-993.
    5. Chen, Xiangtao & Li, Juan, 2019. "Community detection in complex networks using edge-deleting with restrictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 181-194.
    6. Wang, Xingyuan & Li, Junqiu, 2013. "Detecting communities by the core-vertex and intimate degree in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2555-2563.
    7. Wang, Dong & Zhao, Yi, 2019. "Network community detection from the perspective of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 205-214.
    8. Wang, Tao & Yin, Liyan & Wang, Xiaoxia, 2018. "A community detection method based on local similarity and degree clustering information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1344-1354.
    9. Bae, Joonhyun & Kim, Sangwook, 2014. "Identifying and ranking influential spreaders in complex networks by neighborhood coreness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 549-559.
    10. Sun, Peng Gang & Sun, Xiya, 2017. "Complete graph model for community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 88-97.
    11. Zhang, Teng & Bu, Changjiang, 2019. "Detecting community structure in complex networks via resistance distance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
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