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Community detection via network node vector label propagation

Author

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  • Luo, Mengdi
  • Xu, Ying

Abstract

The discovery and analysis of community structure in complex networks is a hot topic in recent years. A community detection method called NVP is proposed based on the node vector label propagation rate of network nodes. The algorithm determines the centers by looking for NGC nodes, use the concept that the inner product of the node vector in the approximate modularity is greater than 0 to divide the initial community, and then divides the final community according to the relevant content of the label propagation rate. Experimental results show that the algorithm can effectively identify the community structure of various real-world networks and computer-generated networks. In addition, this algorithm can also obtain higher NMI values than DPC, FG, LE and DCN algorithms.

Suggested Citation

  • Luo, Mengdi & Xu, Ying, 2022. "Community detection via network node vector label propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    • Handle: RePEc:eee:phsmap:v:593:y:2022:i:c:s0378437122000486
    DOI: 10.1016/j.physa.2022.126931
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    References listed on IDEAS

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    3. Deng, Zheng-Hong & Qiao, Hong-Hai & Gao, Ming-Yu & Song, Qun & Gao, Li, 2019. "Complex network community detection method by improved density peaks model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
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