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A label propagation community discovery algorithm combining seed node influence and neighborhood similarity

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Abstract

To address the problem of poor stability and low accuracy of community division caused by the randomness in the traditional label propagation algorithm (LPA), a community discovery algorithm that combines seed node influence and neighborhood similarity is proposed. Firstly, the K-shell values of neighbor nodes are combined with clustering coefficients to define node influence, the initial seed set is filtered by a threshold, and the less influential one in adjacent node pairs is removed to obtain the final seed set. Secondly, the connection strengths between non-seed nodes and seed nodes are defined based on their own weights, distance weights, and common neighbor weights. The labels of non-seed nodes are updated to the labels of seed nodes with which they have the maximum connection strength. Further, for the case that the connection strengths between a non-seed node and multiple seed nodes are the same, a new neighborhood similarity combining the information between the two types of nodes and their neighbors is proposed, thus avoiding the instability caused by randomly selecting the labels of seed nodes. Experiments are conducted on six classic real networks and eight artificial datasets with different complexities. The comparison and analysis with dozens of related algorithms are also done, which shows the proposed algorithm effectively improves the execution efficiency, and the community division results are stable and more accurate, with a maximum improvement in the modularity of about 87.64% and 47.04% over the LPA on real and artificial datasets, respectively.

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Funding

This work was supported by the National Natural Science Foundation of China (No.42002138), Postdoctoral Scientific Research Development Fund of Heilongjiang Province (No.LBH-Q20073), Excellent Young and Middle-aged Innovative Team Cultivation Foundation of Northeast Petroleum University (No.KYCXTDQ202101), and S&T Program of Hebei (No.226Z0102G).

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Authors and Affiliations

  1. School of Computer and Information Technology, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China

    Miaomiao Liu & Jinyun Yang

  2. Key Laboratory of Petroleum Big Data and Intelligent Analysis of Heilongjiang Province, Daqing, 163318, Heilongjiang, China

    Miaomiao Liu

  3. College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Jingfeng Guo & Jing Chen

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  1. Miaomiao Liu
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  4. Jing Chen
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Contributions

Miaomiao Liu designed the algorithm and wrote the main manuscript text. Jinyun Yang performed the experiments and revised the manuscript. Jingfeng Guo and Jing Chen helped design the algorithm. All authors reviewed the manuscript.

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Correspondence to Jinyun Yang.

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Liu, M., Yang, J., Guo, J. et al. A label propagation community discovery algorithm combining seed node influence and neighborhood similarity. Knowl Inf Syst 66, 2625–2649 (2024). https://doi.org/10.1007/s10115-023-02035-w

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