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A new clustering algorithm based on data field in complex networks

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Abstract

The evaluation of node importance in complex networks has been an increasing widespread concern in recent years. Seeking and protecting vital nodes is important to ensure the security and stability of the whole network. Existing clustering algorithms of complex networks all have certain drawbacks, which could not cover everything in calculation accuracy and time complexity, and need external supervision. To design a fast complex networks clustering method is a problem which requires to be solved immediately. This paper proposes a clustering algorithm of complex networks based on data field using physical data field theory, which excavates key nodes in complex networks by evaluating the importance of nodes based on a mutual information algorithm, and then uses it to classify the clusters. To verify the validity of the algorithm, a simulation experiment was conducted. The results indicated that the algorithm could analyze the cluster exactly and calculate with high-speed, it could also determine the granularity of a partition according to the actual demand.

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Authors and Affiliations

  1. School of Computer, Central China Normal University, Wuhan, 430079, China

    Yuhua Liu, Jianzhi Jin, Yi Zhang & Cui Xu

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  1. Yuhua Liu
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  2. Jianzhi Jin
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  3. Yi Zhang
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  4. Cui Xu
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Correspondence to Jianzhi Jin.

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Liu, Y., Jin, J., Zhang, Y. et al. A new clustering algorithm based on data field in complex networks. J Supercomput 67, 723–737 (2014). https://doi.org/10.1007/s11227-013-0984-x

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  • DOI: https://doi.org/10.1007/s11227-013-0984-x

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